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Wikipedia

Social network

This article is about the theoretical concept as used in the social and behavioral sciences. For social networking sites, see Social networking service. For the 2010 movie, see The Social Network. For other uses, see Social network (disambiguation).

A social network is a social structure made up of a set of social actors (such as individuals or organizations), sets of dyadic ties, and other social interactions between actors. The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures. The study of these structures uses social network analysis to identify local and global patterns, locate influential entities, and examine network dynamics.

Social networks and the analysis of them is an inherently interdisciplinary academic field which emerged from social psychology, sociology, statistics, and graph theory. Georg Simmel authored early structural theories in sociology emphasizing the dynamics of triads and "web of group affiliations". Jacob Moreno is credited with developing the first sociograms in the 1930s to study interpersonal relationships. These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the social and behavioral sciences by the 1980s. Social network analysis is now one of the major paradigms in contemporary sociology, and is also employed in a number of other social and formal sciences. Together with other complex networks, it forms part of the nascent field of network science.

Contents

Evolution graph of a social network: Barabási model.

The social network is a theoretical construct useful in the social sciences to study relationships between individuals, groups, organizations, or even entire societies (social units, see differentiation). The term is used to describe a social structure determined by such interactions. The ties through which any given social unit connects represent the convergence of the various social contacts of that unit. This theoretical approach is, necessarily, relational. An axiom of the social network approach to understanding social interaction is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units, instead of the properties of these units themselves. Thus, one common criticism of social network theory is that individual agency is often ignored although this may not be the case in practice (see agent-based modeling). Precisely because many different types of relations, singular or in combination, form these network configurations, network analytics are useful to a broad range of research enterprises. In social science, these fields of study include, but are not limited to anthropology, biology, communication studies, economics, geography, information science, organizational studies, social psychology, sociology, and sociolinguistics.

In the late 1890s, both Émile Durkheim and Ferdinand Tönnies foreshadowed the idea of social networks in their theories and research of social groups. Tönnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief (Gemeinschaft, German, commonly translated as "community") or impersonal, formal, and instrumental social links (Gesellschaft, German, commonly translated as "society"). Durkheim gave a non-individualistic explanation of social facts, arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors. Georg Simmel, writing at the turn of the twentieth century, pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups.

Moreno's sociogram of a 2nd grade class

Major developments in the field can be seen in the 1930s by several groups in psychology, anthropology, and mathematics working independently. In psychology, in the 1930s, Jacob L. Moreno began systematic recording and analysis of social interaction in small groups, especially classrooms and work groups (see sociometry). In anthropology, the foundation for social network theory is the theoretical and ethnographic work of Bronislaw Malinowski, Alfred Radcliffe-Brown, and Claude Lévi-Strauss. A group of social anthropologists associated with Max Gluckman and the Manchester School, including John A. Barnes, J. Clyde Mitchell and Elizabeth Bott Spillius, often are credited with performing some of the first fieldwork from which network analyses were performed, investigating community networks in southern Africa, India and the United Kingdom. Concomitantly, British anthropologist S. F. Nadel codified a theory of social structure that was influential in later network analysis. In sociology, the early (1930s) work of Talcott Parsons set the stage for taking a relational approach to understanding social structure. Later, drawing upon Parsons' theory, the work of sociologist Peter Blau provides a strong impetus for analyzing the relational ties of social units with his work on social exchange theory.

By the 1970s, a growing number of scholars worked to combine the different tracks and traditions. One group consisted of sociologist Harrison White and his students at the Harvard University Department of Social Relations. Also independently active in the Harvard Social Relations department at the time were Charles Tilly, who focused on networks in political and community sociology and social movements, and Stanley Milgram, who developed the "six degrees of separation" thesis. Mark Granovetter and Barry Wellman are among the former students of White who elaborated and championed the analysis of social networks.

Beginning in the late 1990s, social network analysis experienced work by sociologists, political scientists, and physicists such as Duncan J. Watts, Albert-László Barabási, Peter Bearman, Nicholas A. Christakis, James H. Fowler, and others, developing and applying new models and methods to emerging data available about online social networks, as well as "digital traces" regarding face-to-face networks.

Self-organization of a network, based on Nagler, Levina, & Timme, (2011)
Centrality

In general, social networks are self-organizing, emergent, and complex, such that a globally coherent pattern appears from the local interaction of the elements that make up the system. These patterns become more apparent as network size increases. However, a global network analysis of, for example, all interpersonal relationships in the world is not feasible and is likely to contain so much information as to be uninformative. Practical limitations of computing power, ethics and participant recruitment and payment also limit the scope of a social network analysis. The nuances of a local system may be lost in a large network analysis, hence the quality of information may be more important than its scale for understanding network properties. Thus, social networks are analyzed at the scale relevant to the researcher's theoretical question. Although levels of analysis are not necessarily mutually exclusive, there are three general levels into which networks may fall: micro-level, meso-level, and macro-level.

Micro level

At the micro-level, social network research typically begins with an individual, snowballing as social relationships are traced, or may begin with a small group of individuals in a particular social context.

Dyadic level: A dyad is a social relationship between two individuals. Network research on dyads may concentrate on structure of the relationship (e.g. multiplexity, strength), social equality, and tendencies toward reciprocity/mutuality.

Triadic level: Add one individual to a dyad, and you have a triad. Research at this level may concentrate on factors such as balance and transitivity, as well as social equality and tendencies toward reciprocity/mutuality. In the balance theory of Fritz Heider the triad is the key to social dynamics. The discord in a rivalrous love triangle is an example of an unbalanced triad, likely to change to a balanced triad by a change in one of the relations. The dynamics of social friendships in society has been modeled by balancing triads. The study is carried forward with the theory of signed graphs.

Actor level: The smallest unit of analysis in a social network is an individual in their social setting, i.e., an "actor" or "ego". Egonetwork analysis focuses on network characteristics such as size, relationship strength, density, centrality, prestige and roles such as isolates, liaisons, and bridges. Such analyses, are most commonly used in the fields of psychology or social psychology, ethnographic kinship analysis or other genealogical studies of relationships between individuals.

Subset level: Subset levels of network research problems begin at the micro-level, but may cross over into the meso-level of analysis. Subset level research may focus on distance and reachability, cliques, cohesive subgroups, or other group actions or behavior.

Meso level

In general, meso-level theories begin with a population size that falls between the micro- and macro-levels. However, meso-level may also refer to analyses that are specifically designed to reveal connections between micro- and macro-levels. Meso-level networks are low density and may exhibit causal processes distinct from interpersonal micro-level networks.

Social network diagram, meso-level

Organizations: Formal organizations are social groups that distribute tasks for a collective goal. Network research on organizations may focus on either intra-organizational or inter-organizational ties in terms of formal or informal relationships. Intra-organizational networks themselves often contain multiple levels of analysis, especially in larger organizations with multiple branches, franchises or semi-autonomous departments. In these cases, research is often conducted at a work group level and organization level, focusing on the interplay between the two structures. Experiments with networked groups online have documented ways to optimize group-level coordination through diverse interventions, including the addition of autonomous agents to the groups.

Randomly distributed networks: Exponential random graph models of social networks became state-of-the-art methods of social network analysis in the 1980s. This framework has the capacity to represent social-structural effects commonly observed in many human social networks, including general degree-based structural effects commonly observed in many human social networks as well as reciprocity and transitivity, and at the node-level, homophily and attribute-based activity and popularity effects, as derived from explicit hypotheses about dependencies among network ties. Parameters are given in terms of the prevalence of small subgraph configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges. These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro-networks, allowing models to be built from theoretical structural foundations of social behavior.

Examples of a random network and a scale-free network. Each graph has 32 nodes and 32 links. Note the "hubs" (shaded) in the scale-free diagram (on the right).

Scale-free networks: A scale-free network is a network whose degree distribution follows a power law, at least asymptotically. In network theory a scale-free ideal network is a random network with a degree distribution that unravels the size distribution of social groups. Specific characteristics of scale-free networks vary with the theories and analytical tools used to create them, however, in general, scale-free networks have some common characteristics. One notable characteristic in a scale-free network is the relative commonness of vertices with a degree that greatly exceeds the average. The highest-degree nodes are often called "hubs", and may serve specific purposes in their networks, although this depends greatly on the social context. Another general characteristic of scale-free networks is the clustering coefficient distribution, which decreases as the node degree increases. This distribution also follows a power law. The Barabási model of network evolution shown above is an example of a scale-free network.

Macro level

Rather than tracing interpersonal interactions, macro-level analyses generally trace the outcomes of interactions, such as economic or other resource transfer interactions over a large population.

Diagram: section of a large-scale social network

Large-scale networks: Large-scale network is a term somewhat synonymous with "macro-level" as used, primarily, in social and behavioral sciences, in economics. Originally, the term was used extensively in the computer sciences (see large-scale network mapping).

Complex networks: Most larger social networks display features of social complexity, which involves substantial non-trivial features of network topology, with patterns of complex connections between elements that are neither purely regular nor purely random (see, complexity science, dynamical system and chaos theory), as do biological, and technological networks. Such complex network features include a heavy tail in the degree distribution, a high clustering coefficient, assortativity or disassortativity among vertices, community structure (see stochastic block model), and hierarchical structure. In the case of agency-directed networks these features also include reciprocity, triad significance profile (TSP, see network motif), and other features. In contrast, many of the mathematical models of networks that have been studied in the past, such as lattices and random graphs, do not show these features.

Imported theories

Various theoretical frameworks have been imported for the use of social network analysis. The most prominent of these are Graph theory, Balance theory, Social comparison theory, and more recently, the Social identity approach.

Indigenous theories

Few complete theories have been produced from social network analysis. Two that have are structural role theory and heterophily theory.

The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation, as cliques have a tendency to have more homogeneous opinions as well as share many common traits. This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place. However, being similar, each member of the clique would also know more or less what the other members knew. To find new information or insights, members of the clique will have to look beyond the clique to its other friends and acquaintances. This is what Granovetter called "the strength of weak ties".

In the context of networks, social capital exists where people have an advantage because of their location in a network. Contacts in a network provide information, opportunities and perspectives that can be beneficial to the central player in the network. Most social structures tend to be characterized by dense clusters of strong connections. Information within these clusters tends to be rather homogeneous and redundant. Non-redundant information is most often obtained through contacts in different clusters. When two separate clusters possess non-redundant information, there is said to be a structural hole between them. Thus, a network that bridges structural holes will provide network benefits that are in some degree additive, rather than overlapping. An ideal network structure has a vine and cluster structure, providing access to many different clusters and structural holes.

Networks rich in structural holes are a form of social capital in that they offer information benefits. The main player in a network that bridges structural holes is able to access information from diverse sources and clusters. For example, in business networks, this is beneficial to an individual's career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries/sectors. This concept is similar to Mark Granovetter's theory of weak ties, which rests on the basis that having a broad range of contacts is most effective for job attainment.

Art Networks

Research has used network analysis to examine networks created when artists are exhibited together in museum exhibition. Such networks have been shown to affect an artist's recognition in history and historical narratives, even when controlling for individual accomplishments of the artist. Other work examines how network grouping of artists can affect an individual artist's auction performance. An artist's status has been shown to increase when associated with higher status networks, though this association has diminishing returns over an artist's career.

Communication

Communication Studies are often considered a part of both the social sciences and the humanities, drawing heavily on fields such as sociology, psychology, anthropology, information science, biology, political science, and economics as well as rhetoric, literary studies, and semiotics. Many communication concepts describe the transfer of information from one source to another, and can thus be conceived of in terms of a network. Social network analysis has thus been successfully applied to phenomena ranging from the social diffusion of linguistic innovation to the influence of peer learner communication on study abroad second language acquisition.

Community

In J.A. Barnes' day, a "community" referred to a specific geographic location and studies of community ties had to do with who talked, associated, traded, and attended church with whom. Today, however, there are extended "online" communities developed through telecommunications devices and social network services. Such devices and services require extensive and ongoing maintenance and analysis, often using network science methods. Community development studies, today, also make extensive use of such methods.

Complex networks

Complex networks require methods specific to modelling and interpreting social complexity and complex adaptive systems, including techniques of dynamic network analysis. Mechanisms such as Dual-phase evolution explain how temporal changes in connectivity contribute to the formation of structure in social networks.

Conflict and Cooperation

The study of social networks is being used to examine the nature of interdependencies between actors and the ways in which these are related to outcomes of conflict and cooperation. Areas of study include cooperative behavior among participants in collective actions such as protests; promotion of peaceful behavior, social norms, and public goods within communities through networks of informal governance; the role of social networks in both intrastate conflict and interstate conflict; and social networking among politicians, constituents, and bureaucrats.

Criminal networks

In criminology and urban sociology, much attention has been paid to the social networks among criminal actors. For example, murders can be seen as a series of exchanges between gangs. Murders can be seen to diffuse outwards from a single source, because weaker gangs cannot afford to kill members of stronger gangs in retaliation, but must commit other violent acts to maintain their reputation for strength.

Diffusion of innovations

Diffusion of ideas and innovations studies focus on the spread and use of ideas from one actor to another or one culture and another. This line of research seeks to explain why some become "early adopters" of ideas and innovations, and links social network structure with facilitating or impeding the spread of an innovation. A case in point is the social diffusion of linguistic innovation such as neologisms.

Demography

In demography, the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate (for example, homeless people or intravenous drug users.) For example, respondent driven sampling is a network-based sampling technique that relies on respondents to a survey recommending further respondents.

Economic sociology

The field of sociology focuses almost entirely on networks of outcomes of social interactions. More narrowly, economic sociology considers behavioral interactions of individuals and groups through social capital and social "markets". Sociologists, such as Mark Granovetter, have developed core principles about the interactions of social structure, information, ability to punish or reward, and trust that frequently recur in their analyses of political, economic and other institutions. Granovetter examines how social structures and social networks can affect economic outcomes like hiring, price, productivity and innovation and describes sociologists' contributions to analyzing the impact of social structure and networks on the economy.

Health care

Analysis of social networks is increasingly incorporated into health care analytics, not only in epidemiological studies but also in models of patient communication and education, disease prevention, mental health diagnosis and treatment, and in the study of health care organizations and systems.

Human ecology

Human ecology is an interdisciplinary and transdisciplinary study of the relationship between humans and their natural, social, and built environments. The scientific philosophy of human ecology has a diffuse history with connections to geography, sociology, psychology, anthropology, zoology, and natural ecology.

Language and linguistics

Studies of language and linguistics, particularly evolutionary linguistics, focus on the development of linguistic forms and transfer of changes, sounds or words, from one language system to another through networks of social interaction. Social networks are also important in language shift, as groups of people add and/or abandon languages to their repertoire, in the social diffusion of linguistic innovation, and in analyses of second language acquisition via communication with peers.

Literary networks

In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo, De Nooy, and Senekal, to study various aspects of how literature functions. The basic premise is that polysystem theory, which has been around since the writings of Even-Zohar, can be integrated with network theory and the relationships between different actors in the literary network, e.g. writers, critics, publishers, literary histories, etc., can be mapped using visualization from SNA.

Organizational studies

Research studies of formal or informal organization relationships, organizational communication, economics, economic sociology, and other resource transfers. Social networks have also been used to examine how organizations interact with each other, characterizing the many informal connections that link executives together, as well as associations and connections between individual employees at different organizations. Many organizational social network studies focus on teams. Within team network studies, research assesses, for example, the predictors and outcomes of centrality and power, density and centralization of team instrumental and expressive ties, and the role of between-team networks. Intra-organizational networks have been found to affect organizational commitment, organizational identification, interpersonal citizenship behaviour.

Social capital

Social capital is a form of economic and cultural capital in which social networks are central, transactions are marked by reciprocity, trust, and cooperation, and market agents produce goods and services not mainly for themselves, but for a common good. Social capital is split into three dimensions: the structural, the relational and the cognitive dimension. The structural dimension describes how partners interact with each other and which specific partners meet in a social network. Also The structural dimension of social capital indicates the level of ties among organizations. This dimension is highly connected to the relational dimension which refers to trustworthiness, norms, expectations and identifications of the bonds between partners. The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations. The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions.

Social capital is a sociological concept about the value of social relations and the role of cooperation and confidence to achieve positive outcomes. The term refers to the value one can get from their social ties. For example, newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting (e.g., because of unfamiliarity with the local language). A positive relationship exists between social capital and the intensity of social network use. In a dynamic framework, higher activity in a network feeds into higher social capital which itself encourages more activity.

Social media security and privacy

Trust has been well acknowledged as a decisive aspect for the success of social media platforms. In a survey conducted in 2017 that involved 9,000 users, 40% of respondents reported that they deleted their social media accounts because they did not trust that the platforms can protect their private information. 53% of the online users concerned about online privacy in contrast to a year ago as of February 2019 . 81% of the online users in the United States felt that their personal information is vulnerable to hackers as of July 2019 . Almost 16.7 million United States citizens were victims of identity theft in 2017. People and organizations have an incentive to take advantage of social media platforms. A certain type of propaganda can be published to obtain an unrealistic level of influence. It is easy to specify a certain group on social media platforms and attack them based on a prepared strategy. In these days, it has been a popular phenomenon that social media users purchase Twitter followers, Facebook likes, Amazon reviews and YouTube comments. These services can be achieved through strategies such as creating multiple fake profiles, employing compromised accounts, or even paying users to post content over their accounts. Deception in social media can be divided into content deception and identity deception. The content deception is to manipulate social media content by tampering with images, spreading spams and sending malicious links. The content deception mostly happens on social media platforms, such as social news sites and blogs. The identity deception is to manipulate the user’s identity information or to impersonate someone’s identity to deceive social media users.

Advertising

This particular cluster focuses on brand-image and promotional strategy effectiveness, taking into account the impact of customer participation on sales and brand-image. This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics. This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales.

Network position and benefits

In many organizations, members tend to focus their activities inside their own groups, which stifles creativity and restricts opportunities. A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities. Such a player can mobilize social capital by acting as a "broker" of information between two clusters that otherwise would not have been in contact, thus providing access to new ideas, opinions and opportunities. British philosopher and political economist John Stuart Mill, writes, "it is hardly possible to overrate the value ... of placing human beings in contact with persons dissimilar to themselves.... Such communication [is] one of the primary sources of progress." Thus, a player with a network rich in structural holes can add value to an organization through new ideas and opportunities. This in turn, helps an individual's career development and advancement.

A social capital broker also reaps control benefits of being the facilitator of information flow between contacts. In the case of consulting firm Eden McCallum, the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid-size industry firms. By bridging structural holes and mobilizing social capital, players can advance their careers by executing new opportunities between contacts.

There has been research that both substantiates and refutes the benefits of information brokerage. A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are "dissonant to the dominant firm-wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values" of such organizations. However, this study only analyzed Chinese firms, which tend to have strong communal sharing values. Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm-wide level. In 2004, Ronald Burt studied 673 managers who ran the supply chain for one of America's largest electronics companies. He found that managers who often discussed issues with other groups were better paid, received more positive job evaluations and were more likely to be promoted. Thus, bridging structural holes can be beneficial to an organization, and in turn, to an individual's career.

Social media

Main article: Social media

Computer networks combined with social networking software produce a new medium for social interaction. A relationship over a computerized social networking service can be characterized by context, direction, and strength. The content of a relation refers to the resource that is exchanged. In a computer mediated communication context, social pairs exchange different kinds of information, including sending a data file or a computer program as well as providing emotional support or arranging a meeting. With the rise of electronic commerce, information exchanged may also correspond to exchanges of money, goods or services in the "real" world. Social network analysis methods have become essential to examining these types of computer mediated communication.

In addition, the sheer size and the volatile nature of social media has given rise to new network metrics. A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data.

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Social network
Social network Language Watch Edit This article is about the theoretical concept as used in the social and behavioral sciences For social networking sites see Social networking service For the 2010 movie see The Social Network For other uses see Social network disambiguation A social network is a social structure made up of a set of social actors such as individuals or organizations sets of dyadic ties and other social interactions between actors The social network perspective provides a set of methods for analyzing the structure of whole social entities as well as a variety of theories explaining the patterns observed in these structures 1 The study of these structures uses social network analysis to identify local and global patterns locate influential entities and examine network dynamics Social networks and the analysis of them is an inherently interdisciplinary academic field which emerged from social psychology sociology statistics and graph theory Georg Simmel authored early structural theories in sociology emphasizing the dynamics of triads and web of group affiliations 2 Jacob Moreno is credited with developing the first sociograms in the 1930s to study interpersonal relationships These approaches were mathematically formalized in the 1950s and theories and methods of social networks became pervasive in the social and behavioral sciences by the 1980s 1 3 Social network analysis is now one of the major paradigms in contemporary sociology and is also employed in a number of other social and formal sciences Together with other complex networks it forms part of the nascent field of network science 4 5 Contents 1 Overview 2 History 3 Levels of analysis 3 1 Micro level 3 2 Meso level 3 3 Macro level 4 Theoretical links 4 1 Imported theories 4 2 Indigenous theories 5 Structural holes 6 Research clusters 6 1 Art Networks 6 2 Communication 6 3 Community 6 4 Complex networks 6 5 Conflict and Cooperation 6 6 Criminal networks 6 7 Diffusion of innovations 6 8 Demography 6 9 Economic sociology 6 10 Health care 6 11 Human ecology 6 12 Language and linguistics 6 13 Literary networks 6 14 Organizational studies 6 15 Social capital 6 16 Social media security and privacy 6 17 Advertising 6 18 Network position and benefits 6 19 Social media 7 See also 8 References 9 Further reading 10 External links 10 1 Organizations 10 2 Peer reviewed journals 10 3 Textbooks and educational resources 10 4 Data setsOverview Edit Evolution graph of a social network Barabasi model The social network is a theoretical construct useful in the social sciences to study relationships between individuals groups organizations or even entire societies social units see differentiation The term is used to describe a social structure determined by such interactions The ties through which any given social unit connects represent the convergence of the various social contacts of that unit This theoretical approach is necessarily relational An axiom of the social network approach to understanding social interaction is that social phenomena should be primarily conceived and investigated through the properties of relations between and within units instead of the properties of these units themselves Thus one common criticism of social network theory is that individual agency is often ignored 6 although this may not be the case in practice see agent based modeling Precisely because many different types of relations singular or in combination form these network configurations network analytics are useful to a broad range of research enterprises In social science these fields of study include but are not limited to anthropology biology communication studies economics geography information science organizational studies social psychology sociology and sociolinguistics History EditIn the late 1890s both Emile Durkheim and Ferdinand Tonnies foreshadowed the idea of social networks in their theories and research of social groups Tonnies argued that social groups can exist as personal and direct social ties that either link individuals who share values and belief Gemeinschaft German commonly translated as community or impersonal formal and instrumental social links Gesellschaft German commonly translated as society 7 Durkheim gave a non individualistic explanation of social facts arguing that social phenomena arise when interacting individuals constitute a reality that can no longer be accounted for in terms of the properties of individual actors 8 Georg Simmel writing at the turn of the twentieth century pointed to the nature of networks and the effect of network size on interaction and examined the likelihood of interaction in loosely knit networks rather than groups 9 Moreno s sociogram of a 2nd grade class Major developments in the field can be seen in the 1930s by several groups in psychology anthropology and mathematics working independently 6 10 11 In psychology in the 1930s Jacob L Moreno began systematic recording and analysis of social interaction in small groups especially classrooms and work groups see sociometry In anthropology the foundation for social network theory is the theoretical and ethnographic work of Bronislaw Malinowski 12 Alfred Radcliffe Brown 13 14 and Claude Levi Strauss 15 A group of social anthropologists associated with Max Gluckman and the Manchester School including John A Barnes 16 J Clyde Mitchell and Elizabeth Bott Spillius 17 18 often are credited with performing some of the first fieldwork from which network analyses were performed investigating community networks in southern Africa India and the United Kingdom 6 Concomitantly British anthropologist S F Nadel codified a theory of social structure that was influential in later network analysis 19 In sociology the early 1930s work of Talcott Parsons set the stage for taking a relational approach to understanding social structure 20 21 Later drawing upon Parsons theory the work of sociologist Peter Blau provides a strong impetus for analyzing the relational ties of social units with his work on social exchange theory 22 23 24 By the 1970s a growing number of scholars worked to combine the different tracks and traditions One group consisted of sociologist Harrison White and his students at the Harvard University Department of Social Relations Also independently active in the Harvard Social Relations department at the time were Charles Tilly who focused on networks in political and community sociology and social movements and Stanley Milgram who developed the six degrees of separation thesis 25 Mark Granovetter 26 and Barry Wellman 27 are among the former students of White who elaborated and championed the analysis of social networks 26 28 29 30 Beginning in the late 1990s social network analysis experienced work by sociologists political scientists and physicists such as Duncan J Watts Albert Laszlo Barabasi Peter Bearman Nicholas A Christakis James H Fowler and others developing and applying new models and methods to emerging data available about online social networks as well as digital traces regarding face to face networks Levels of analysis Edit Self organization of a network based on Nagler Levina amp Timme 2011 31 Centrality In general social networks are self organizing emergent and complex such that a globally coherent pattern appears from the local interaction of the elements that make up the system 32 33 These patterns become more apparent as network size increases However a global network analysis 34 of for example all interpersonal relationships in the world is not feasible and is likely to contain so much information as to be uninformative Practical limitations of computing power ethics and participant recruitment and payment also limit the scope of a social network analysis 35 36 The nuances of a local system may be lost in a large network analysis hence the quality of information may be more important than its scale for understanding network properties Thus social networks are analyzed at the scale relevant to the researcher s theoretical question Although levels of analysis are not necessarily mutually exclusive there are three general levels into which networks may fall micro level meso level and macro level Micro level Edit At the micro level social network research typically begins with an individual snowballing as social relationships are traced or may begin with a small group of individuals in a particular social context Dyadic level A dyad is a social relationship between two individuals Network research on dyads may concentrate on structure of the relationship e g multiplexity strength social equality and tendencies toward reciprocity mutuality Triadic level Add one individual to a dyad and you have a triad Research at this level may concentrate on factors such as balance and transitivity as well as social equality and tendencies toward reciprocity mutuality 35 In the balance theory of Fritz Heider the triad is the key to social dynamics The discord in a rivalrous love triangle is an example of an unbalanced triad likely to change to a balanced triad by a change in one of the relations The dynamics of social friendships in society has been modeled by balancing triads The study is carried forward with the theory of signed graphs Actor level The smallest unit of analysis in a social network is an individual in their social setting i e an actor or ego Egonetwork analysis focuses on network characteristics such as size relationship strength density centrality prestige and roles such as isolates liaisons and bridges 37 Such analyses are most commonly used in the fields of psychology or social psychology ethnographic kinship analysis or other genealogical studies of relationships between individuals Subset level Subset levels of network research problems begin at the micro level but may cross over into the meso level of analysis Subset level research may focus on distance and reachability cliques cohesive subgroups or other group actions or behavior 38 Meso level Edit In general meso level theories begin with a population size that falls between the micro and macro levels However meso level may also refer to analyses that are specifically designed to reveal connections between micro and macro levels Meso level networks are low density and may exhibit causal processes distinct from interpersonal micro level networks 39 Social network diagram meso level Organizations Formal organizations are social groups that distribute tasks for a collective goal 40 Network research on organizations may focus on either intra organizational or inter organizational ties in terms of formal or informal relationships Intra organizational networks themselves often contain multiple levels of analysis especially in larger organizations with multiple branches franchises or semi autonomous departments In these cases research is often conducted at a work group level and organization level focusing on the interplay between the two structures 40 Experiments with networked groups online have documented ways to optimize group level coordination through diverse interventions including the addition of autonomous agents to the groups 41 Randomly distributed networks Exponential random graph models of social networks became state of the art methods of social network analysis in the 1980s This framework has the capacity to represent social structural effects commonly observed in many human social networks including general degree based structural effects commonly observed in many human social networks as well as reciprocity and transitivity and at the node level homophily and attribute based activity and popularity effects as derived from explicit hypotheses about dependencies among network ties Parameters are given in terms of the prevalence of small subgraph configurations in the network and can be interpreted as describing the combinations of local social processes from which a given network emerges These probability models for networks on a given set of actors allow generalization beyond the restrictive dyadic independence assumption of micro networks allowing models to be built from theoretical structural foundations of social behavior 42 Examples of a random network and a scale free network Each graph has 32 nodes and 32 links Note the hubs shaded in the scale free diagram on the right Scale free networks A scale free network is a network whose degree distribution follows a power law at least asymptotically In network theory a scale free ideal network is a random network with a degree distribution that unravels the size distribution of social groups 43 Specific characteristics of scale free networks vary with the theories and analytical tools used to create them however in general scale free networks have some common characteristics One notable characteristic in a scale free network is the relative commonness of vertices with a degree that greatly exceeds the average The highest degree nodes are often called hubs and may serve specific purposes in their networks although this depends greatly on the social context Another general characteristic of scale free networks is the clustering coefficient distribution which decreases as the node degree increases This distribution also follows a power law 44 The Barabasi model of network evolution shown above is an example of a scale free network Macro level Edit Rather than tracing interpersonal interactions macro level analyses generally trace the outcomes of interactions such as economic or other resource transfer interactions over a large population Diagram section of a large scale social network Large scale networks Large scale network is a term somewhat synonymous with macro level as used primarily in social and behavioral sciences in economics Originally the term was used extensively in the computer sciences see large scale network mapping Complex networks Most larger social networks display features of social complexity which involves substantial non trivial features of network topology with patterns of complex connections between elements that are neither purely regular nor purely random see complexity science dynamical system and chaos theory as do biological and technological networks Such complex network features include a heavy tail in the degree distribution a high clustering coefficient assortativity or disassortativity among vertices community structure see stochastic block model and hierarchical structure In the case of agency directed networks these features also include reciprocity triad significance profile TSP see network motif and other features In contrast many of the mathematical models of networks that have been studied in the past such as lattices and random graphs do not show these features 45 Theoretical links EditImported theories Edit Various theoretical frameworks have been imported for the use of social network analysis The most prominent of these are Graph theory Balance theory Social comparison theory and more recently the Social identity approach 46 Indigenous theories Edit Few complete theories have been produced from social network analysis Two that have are structural role theory and heterophily theory The basis of Heterophily Theory was the finding in one study that more numerous weak ties can be important in seeking information and innovation as cliques have a tendency to have more homogeneous opinions as well as share many common traits This homophilic tendency was the reason for the members of the cliques to be attracted together in the first place However being similar each member of the clique would also know more or less what the other members knew To find new information or insights members of the clique will have to look beyond the clique to its other friends and acquaintances This is what Granovetter called the strength of weak ties 47 Structural holes EditIn the context of networks social capital exists where people have an advantage because of their location in a network Contacts in a network provide information opportunities and perspectives that can be beneficial to the central player in the network Most social structures tend to be characterized by dense clusters of strong connections 48 Information within these clusters tends to be rather homogeneous and redundant Non redundant information is most often obtained through contacts in different clusters 49 When two separate clusters possess non redundant information there is said to be a structural hole between them 49 Thus a network that bridges structural holes will provide network benefits that are in some degree additive rather than overlapping An ideal network structure has a vine and cluster structure providing access to many different clusters and structural holes 49 Networks rich in structural holes are a form of social capital in that they offer information benefits The main player in a network that bridges structural holes is able to access information from diverse sources and clusters 49 For example in business networks this is beneficial to an individual s career because he is more likely to hear of job openings and opportunities if his network spans a wide range of contacts in different industries sectors This concept is similar to Mark Granovetter s theory of weak ties which rests on the basis that having a broad range of contacts is most effective for job attainment Research clusters EditArt Networks Edit Research has used network analysis to examine networks created when artists are exhibited together in museum exhibition Such networks have been shown to affect an artist s recognition in history and historical narratives even when controlling for individual accomplishments of the artist 50 51 Other work examines how network grouping of artists can affect an individual artist s auction performance 52 An artist s status has been shown to increase when associated with higher status networks though this association has diminishing returns over an artist s career Communication Edit Communication Studies are often considered a part of both the social sciences and the humanities drawing heavily on fields such as sociology psychology anthropology information science biology political science and economics as well as rhetoric literary studies and semiotics Many communication concepts describe the transfer of information from one source to another and can thus be conceived of in terms of a network Social network analysis has thus been successfully applied to phenomena ranging from the social diffusion of linguistic innovation 53 to the influence of peer learner communication on study abroad second language acquisition 54 Community Edit In J A Barnes day a community referred to a specific geographic location and studies of community ties had to do with who talked associated traded and attended church with whom Today however there are extended online communities developed through telecommunications devices and social network services Such devices and services require extensive and ongoing maintenance and analysis often using network science methods Community development studies today also make extensive use of such methods Complex networks Edit Complex networks require methods specific to modelling and interpreting social complexity and complex adaptive systems including techniques of dynamic network analysis Mechanisms such as Dual phase evolution explain how temporal changes in connectivity contribute to the formation of structure in social networks Conflict and Cooperation Edit The study of social networks is being used to examine the nature of interdependencies between actors and the ways in which these are related to outcomes of conflict and cooperation Areas of study include cooperative behavior among participants in collective actions such as protests promotion of peaceful behavior social norms and public goods within communities through networks of informal governance the role of social networks in both intrastate conflict and interstate conflict and social networking among politicians constituents and bureaucrats 55 Criminal networks Edit In criminology and urban sociology much attention has been paid to the social networks among criminal actors For example murders can be seen as a series of exchanges between gangs Murders can be seen to diffuse outwards from a single source because weaker gangs cannot afford to kill members of stronger gangs in retaliation but must commit other violent acts to maintain their reputation for strength 56 Diffusion of innovations Edit Diffusion of ideas and innovations studies focus on the spread and use of ideas from one actor to another or one culture and another This line of research seeks to explain why some become early adopters of ideas and innovations and links social network structure with facilitating or impeding the spread of an innovation A case in point is the social diffusion of linguistic innovation such as neologisms 53 Demography Edit In demography the study of social networks has led to new sampling methods for estimating and reaching populations that are hard to enumerate for example homeless people or intravenous drug users For example respondent driven sampling is a network based sampling technique that relies on respondents to a survey recommending further respondents 57 58 Economic sociology Edit The field of sociology focuses almost entirely on networks of outcomes of social interactions More narrowly economic sociology considers behavioral interactions of individuals and groups through social capital and social markets Sociologists such as Mark Granovetter have developed core principles about the interactions of social structure information ability to punish or reward and trust that frequently recur in their analyses of political economic and other institutions Granovetter examines how social structures and social networks can affect economic outcomes like hiring price productivity and innovation and describes sociologists contributions to analyzing the impact of social structure and networks on the economy 59 Health care Edit Analysis of social networks is increasingly incorporated into health care analytics not only in epidemiological studies but also in models of patient communication and education disease prevention mental health diagnosis and treatment and in the study of health care organizations and systems 60 Human ecology Edit Human ecology is an interdisciplinary and transdisciplinary study of the relationship between humans and their natural social and built environments The scientific philosophy of human ecology has a diffuse history with connections to geography sociology psychology anthropology zoology and natural ecology 61 62 Language and linguistics Edit Studies of language and linguistics particularly evolutionary linguistics focus on the development of linguistic forms and transfer of changes sounds or words from one language system to another through networks of social interaction Social networks are also important in language shift as groups of people add and or abandon languages to their repertoire in the social diffusion of linguistic innovation 53 and in analyses of second language acquisition via communication with peers 54 Literary networks Edit In the study of literary systems network analysis has been applied by Anheier Gerhards and Romo 63 De Nooy 64 and Senekal 65 to study various aspects of how literature functions The basic premise is that polysystem theory which has been around since the writings of Even Zohar can be integrated with network theory and the relationships between different actors in the literary network e g writers critics publishers literary histories etc can be mapped using visualization from SNA Organizational studies Edit Research studies of formal or informal organization relationships organizational communication economics economic sociology and other resource transfers Social networks have also been used to examine how organizations interact with each other characterizing the many informal connections that link executives together as well as associations and connections between individual employees at different organizations 66 Many organizational social network studies focus on teams 67 Within team network studies research assesses for example the predictors and outcomes of centrality and power density and centralization of team instrumental and expressive ties and the role of between team networks Intra organizational networks have been found to affect organizational commitment 68 organizational identification 37 interpersonal citizenship behaviour 69 Social capital Edit Social capital is a form of economic and cultural capital in which social networks are central transactions are marked by reciprocity trust and cooperation and market agents produce goods and services not mainly for themselves but for a common good Social capital is split into three dimensions the structural the relational and the cognitive dimension The structural dimension describes how partners interact with each other and which specific partners meet in a social network Also The structural dimension of social capital indicates the level of ties among organizations 70 This dimension is highly connected to the relational dimension which refers to trustworthiness norms expectations and identifications of the bonds between partners The relational dimension explains the nature of these ties which is mainly illustrated by the level of trust accorded to the network of organizations 70 The cognitive dimension analyses the extent to which organizations share common goals and objectives as a result of their ties and interactions 70 Social capital is a sociological concept about the value of social relations and the role of cooperation and confidence to achieve positive outcomes The term refers to the value one can get from their social ties For example newly arrived immigrants can make use of their social ties to established migrants to acquire jobs they may otherwise have trouble getting e g because of unfamiliarity with the local language A positive relationship exists between social capital and the intensity of social network use 71 72 73 In a dynamic framework higher activity in a network feeds into higher social capital which itself encourages more activity 71 74 Social media security and privacy Edit Trust has been well acknowledged as a decisive aspect for the success of social media platforms In a survey conducted in 2017 that involved 9 000 users 40 of respondents reported that they deleted their social media accounts because they did not trust that the platforms can protect their private information 53 of the online users concerned about online privacy in contrast to a year ago as of February 2019 81 of the online users in the United States felt that their personal information is vulnerable to hackers as of July 2019 Almost 16 7 million United States citizens were victims of identity theft in 2017 People and organizations have an incentive to take advantage of social media platforms A certain type of propaganda can be published to obtain an unrealistic level of influence It is easy to specify a certain group on social media platforms and attack them based on a prepared strategy In these days it has been a popular phenomenon that social media users purchase Twitter followers Facebook likes Amazon reviews and YouTube comments These services can be achieved through strategies such as creating multiple fake profiles employing compromised accounts or even paying users to post content over their accounts Deception in social media can be divided into content deception and identity deception The content deception is to manipulate social media content by tampering with images spreading spams and sending malicious links The content deception mostly happens on social media platforms such as social news sites and blogs The identity deception is to manipulate the user s identity information or to impersonate someone s identity to deceive social media users 75 Advertising Edit This particular cluster focuses on brand image and promotional strategy effectiveness taking into account the impact of customer participation on sales and brand image This is gauged through techniques such as sentiment analysis which rely on mathematical areas of study such as data mining and analytics This area of research produces vast numbers of commercial applications as the main goal of any study is to understand consumer behaviour and drive sales Network position and benefits Edit In many organizations members tend to focus their activities inside their own groups which stifles creativity and restricts opportunities A player whose network bridges structural holes has an advantage in detecting and developing rewarding opportunities 48 Such a player can mobilize social capital by acting as a broker of information between two clusters that otherwise would not have been in contact thus providing access to new ideas opinions and opportunities British philosopher and political economist John Stuart Mill writes it is hardly possible to overrate the value of placing human beings in contact with persons dissimilar to themselves Such communication is one of the primary sources of progress 76 Thus a player with a network rich in structural holes can add value to an organization through new ideas and opportunities This in turn helps an individual s career development and advancement A social capital broker also reaps control benefits of being the facilitator of information flow between contacts In the case of consulting firm Eden McCallum the founders were able to advance their careers by bridging their connections with former big three consulting firm consultants and mid size industry firms 77 By bridging structural holes and mobilizing social capital players can advance their careers by executing new opportunities between contacts There has been research that both substantiates and refutes the benefits of information brokerage A study of high tech Chinese firms by Zhixing Xiao found that the control benefits of structural holes are dissonant to the dominant firm wide spirit of cooperation and the information benefits cannot materialize due to the communal sharing values of such organizations 78 However this study only analyzed Chinese firms which tend to have strong communal sharing values Information and control benefits of structural holes are still valuable in firms that are not quite as inclusive and cooperative on the firm wide level In 2004 Ronald Burt studied 673 managers who ran the supply chain for one of America s largest electronics companies He found that managers who often discussed issues with other groups were better paid received more positive job evaluations and were more likely to be promoted 48 Thus bridging structural holes can be beneficial to an organization and in turn to an individual s career Social media Edit Main article Social media Computer networks combined with social networking software produce a new medium for social interaction 79 A relationship over a computerized social networking service can be characterized by context direction and strength The content of a relation refers to the resource that is exchanged In a computer mediated communication context social pairs exchange different kinds of information including sending a data file or a computer program as well as providing emotional support or arranging a meeting With the rise of electronic commerce information exchanged may also correspond to exchanges of money goods or services in the real world 80 Social network analysis methods have become essential to examining these types of computer mediated communication In addition the sheer size and the volatile nature of social media has given rise to new network metrics A key concern with networks extracted from social media is the lack of robustness of network metrics given missing data 81 See also EditBibliography of sociology Blockmodeling Business networking Collective network International Network for Social Network Analysis Network society Network theory Network science Semiotics of social networking Scientific collaboration network Social network analysis Social network sociolinguistics Social networking service Social web Structural foldReferences Edit a b Wasserman Stanley Faust Katherine 1994 Social Network Analysis in the Social and Behavioral Sciences Social Network Analysis Methods and Applications Cambridge University Press pp 1 27 ISBN 9780521387071 Scott W Richard Davis Gerald F 2003 Networks In and Around Organizations Organizations and Organizing Pearson Prentice Hall ISBN 978 0 13 195893 7 Freeman Linton 2004 The Development of Social Network Analysis A Study in the Sociology of Science Empirical Press ISBN 978 1 59457 714 7 Borgatti Stephen P Mehra Ajay Brass Daniel J Labianca Giuseppe 2009 Network 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Movements The Case of the Swedish Social Democratic Party PDF American Journal of Sociology 106 1 145 172 doi 10 1086 303109 S2CID 3609428 a b Riketta M Nienber S 2007 Multiple identities and work motivation The role of perceived compatibility between nested organizational units British Journal of Management 18 S61 77 doi 10 1111 j 1467 8551 2007 00526 x S2CID 144857162 Shirado Hirokazu Christakis Nicholas A 2017 Locally noisy autonomous agents improve global human coordination in network experiments Nature 545 7654 370 374 Bibcode 2017Natur 545 370S doi 10 1038 nature22332 PMC 5912653 PMID 28516927 Cranmer Skyler J Desmarais Bruce A 2011 Inferential Network Analysis with Exponential Random Graph Models Political Analysis 19 1 66 86 CiteSeerX 10 1 1 623 751 doi 10 1093 pan mpq037 Moreira Andre A Demetrius R Paula Raimundo N Costa Filho Jose S Andrade Jr 2006 Competitive cluster growth in complex networks Physical Review E 73 6 065101 arXiv cond mat 0603272 Bibcode 2006PhRvE 73f5101M doi 10 1103 PhysRevE 73 065101 PMID 16906890 S2CID 45651735 Barabasi Albert Laszlo 2003 Linked how everything is connected to everything else and what it means for business science and everyday life New York Plum Strogatz Steven H 2001 Exploring complex networks Nature 410 6825 268 276 Bibcode 2001Natur 410 268S doi 10 1038 35065725 PMID 11258382 Kilduff M Tsai W 2003 Social networks and organisations Sage Publications Granovetter M 1973 The strength of weak ties American Journal of Sociology 78 6 1360 1380 doi 10 1086 225469 S2CID 59578641 a b c Burt Ronald 2004 Structural Holes and Good Ideas American Journal of Sociology 110 2 349 399 CiteSeerX 10 1 1 388 2251 doi 10 1086 421787 S2CID 2152743 a b c d Burt Ronald 1992 Structural Holes The Social Structure of Competition Cambridge MA Harvard University Press Braden L E A Teekens Thomas 2020 08 01 Historic networks and commemoration Connections created through museum exhibitions Poetics 81 101446 doi 10 1016 j poetic 2020 101446 ISSN 0304 422X Braden L E A 2021 01 01 Networks Created Within Exhibition The Curators Effect on Historical Recognition American Behavioral Scientist 65 1 25 43 doi 10 1177 0002764218800145 ISSN 0002 7642 Braden L E A Teekens Thomas September 2019 Reputation Status Networks and the Art Market Arts 8 3 81 doi 10 3390 arts8030081 a b c Paradowski Michal B Jonak Lukasz 2012 Diffusion of linguistic innovation as social coordination Psychology of Language and Communication 16 2 53 64 doi 10 2478 v10057 012 0010 z a b Paradowski Michal B Jarynowski Andrzej Jelinska Magdalena Czopek Karolina 2021 Out of class peer interactions matter for second language acquisition during short term overseas sojourns The contributions of Social Network Analysis Selected poster presentations from the American Association of Applied Linguistics conference Denver USA March 2020 Language Teaching 54 1 139 143 doi 10 1017 S0261444820000580 Larson Jennifer M 11 May 2021 Networks of Conflict and Cooperation Annual Review of Political Science 24 1 89 107 doi 10 1146 annurev polisci 041719 102523 Papachristos Andrew 2009 Murder by Structure Dominance Relations and the Social Structure of Gang Homicide PDF American Journal of Sociology 115 1 74 128 doi 10 2139 ssrn 855304 PMID 19852186 S2CID 24605697 Archived from the original PDF on 7 April 2014 Retrieved 29 March 2013 Gile Krista J Beaudry Isabelle S Handcock Mark S Ott Miles Q 7 March 2018 Methods for Inference from Respondent Driven Sampling Data Annual Review of Statistics and Its Application 5 1 65 93 Bibcode 2018AnRSA 5 65G doi 10 1146 annurev statistics 031017 100704 Retrieved 21 September 2021 Heckathorn Douglas D Cameron Christopher J 31 July 2017 Network Sampling From Snowball and Multiplicity to Respondent Driven Sampling Annual Review of Sociology 43 1 101 119 doi 10 1146 annurev soc 060116 053556 Retrieved 21 September 2021 Granovetter Mark 2005 The Impact of Social Structure on Economic Outcomes The Journal of Economic Perspectives 19 1 33 50 doi 10 1257 0895330053147958 JSTOR 4134991 Levy Judith and Bernice Pescosolido 2002 Social Networks and Health Boston MA JAI Press Crona Beatrice and Klaus Hubacek eds 2010 Special Issue Social network analysis in natural resource governance Ecology and Society 48 Ernstson Henrich 2010 Reading list Using social network analysis SNA in social ecological studies Resilience Science Anheier H K Romo F P 1995 Forms of capital and social structure of fields examining Bourdieu s social topography American Journal of Sociology 100 4 859 903 doi 10 1086 230603 S2CID 143587142 De Nooy W 2003 Fields and networks Correspondence analysis and social network analysis in the framework of Field Theory Poetics 31 5 6 305 327 doi 10 1016 S0304 422X 03 00035 4 Senekal B A 2012 Die Afrikaanse literere sisteem ʼn Eksperimentele benadering met behulp van Sosiale netwerk analise SNA LitNet Akademies 9 3 Podolny J M Baron J N 1997 Resources and relationships Social networks and mobility in the workplace American Sociological Review 62 5 673 693 CiteSeerX 10 1 1 114 6822 doi 10 2307 2657354 JSTOR 2657354 Park Semin Grosser Travis J Roebuck Adam A Mathieu John E 3 February 2020 Understanding Work Teams From a Network Perspective A Review and Future Research Directions Journal of Management 46 6 1002 1028 doi 10 1177 0149206320901573 Lee J Kim S 2011 Exploring the role of social networks in affective organizational commitment Network centrality strength of ties and structural holes The American Review of Public Administration 41 2 205 223 doi 10 1177 0275074010373803 S2CID 145641976 Bowler W M Brass D J 2011 Relational correlates of interpersonal citizenship behaviour A social network perspective Journal of Applied Psychology 91 1 70 82 CiteSeerX 10 1 1 516 8746 doi 10 1037 0021 9010 91 1 70 PMID 16435939 a b c Claridge 2018 a b Koley Gaurav Deshmukh Jayati Srinivasa Srinath 2020 Aref Samin Bontcheva Kalina Braghieri Marco Dignum Frank Giannotti Fosca Grisolia Francesco Pedreschi Dino eds Social Capital as Engagement and Belief Revision Social Informatics Lecture Notes in Computer Science Cham Springer International Publishing 12467 137 151 doi 10 1007 978 3 030 60975 7 11 ISBN 978 3 030 60975 7 S2CID 222233101 Sebastian Valenzuela Namsu Park Kerk F Kee 2009 Is There Social Capital in a Social Network Site Facebook Use and College Students Life Satisfaction Trust and Participation Journal of Computer Mediated Communication 14 4 875 901 doi 10 1111 j 1083 6101 2009 01474 x Wang Hua amp Barry Wellman 2010 Social Connectivity in America Changes in Adult Friendship Network Size from 2002 to 2007 American Behavioral Scientist 53 8 1148 1169 doi 10 1177 0002764209356247 S2CID 144525876 Gaudeul Alexia Giannetti Caterina 2013 The role of reciprocation in social network formation with an application to LiveJournal Social Networks 35 3 317 330 doi 10 1016 j socnet 2013 03 003 ISSN 0378 8733 Alharbi Ahmed Dong Hai Yi Xun Tari Zahir Khalil Ibrahim May 2021 Social Media Identity Deception Detection A Survey ACM Computing Surveys 54 3 1 35 arXiv 2103 04673 doi 10 1145 3446372 ISSN 0360 0300 S2CID 232147964 Mill John 1909 Principles of Political Economy Library of Economics and Liberty William J Ashley Gardner Heidi Eccles Robert 2011 Eden McCallum A Network Based Consulting Firm Harvard Business School Review Xiao Zhixing Tsui Anne 2007 When Brokers May Not Work The Cultural Contingency of Social Capital in Chinese High tech Firms Administrative Science Quarterly Amichai Hamburger Yair Hayat Tsahi 2017 The International Encyclopedia of Media Effects John Wiley amp Sons Inc doi 10 1002 9781118783764 wbieme0170 ISBN 9781118783764 Garton Laura Haythornthwaite Caroline Wellman Barry 23 June 2006 Studying Online Social Networks Journal of Computer Mediated Communication 3 1 0 doi 10 1111 j 1083 6101 1997 tb00062 x Wei Wei Joseph Kenneth Liu Huan Carley Kathleen M 2016 Exploring Characteristics of Suspended Users and Network Stability on Twitter Social Network Analysis and Mining 6 51 doi 10 1007 s13278 016 0358 5 S2CID 18520393 Further reading EditAneja Nagender Gambhir Sapna August 2013 Ad hoc Social Network A Comprehensive Survey PDF International Journal of Scientific amp Engineering Research 4 8 156 160 ISSN 2229 5518 Barabasi Albert Laszlo 2003 Linked How Everything Is Connected to Everything Else and What It Means for Business Science and Everyday Life Plum ISBN 978 0 452 28439 5 Barnett George A 2011 Encyclopedia of Social Networks Sage ISBN 978 1 4129 7911 5 Estrada E 2011 The Structure of Complex Networks Theory and Applications Oxford University Press ISBN 978 0 199 59175 6 Ferguson Niall 2018 The Square and the Tower Networks and Power from the Freemasons to Facebook Penguin Press ISBN 978 0735222915 Freeman Linton C 2004 The Development of Social Network Analysis A Study in the Sociology of Science Empirical Press ISBN 978 1 59457 714 7 Kadushin Charles 2012 Understanding Social Networks Theories Concepts and Findings Oxford University Press ISBN 978 0 19 537946 4 Mauro Rios Petrella Carlos 2014 La Quimera de las Redes Sociales The Chimera of Social Networks in Spanish Bubok Espana ISBN 978 9974 99 637 3 Rainie Lee Wellman Barry 2012 Networked The New Social Operating System Cambridge Mass MIT Press ISBN 978 0262017190 Scott John 1991 Social Network Analysis A Handbook Sage ISBN 978 0 7619 6338 7 Wasserman Stanley Faust Katherine 1994 Social Network Analysis Methods and Applications Structural Analysis in the Social Sciences Cambridge University Press ISBN 978 0 521 38269 4 Wellman Barry Berkowitz S D 1988 Social Structures A Network Approach Structural Analysis in the Social Sciences Cambridge University Press ISBN 978 0 521 24441 1 External links EditOrganizations Edit International Network for Social Network AnalysisPeer reviewed journals Edit Social Networks Network Science Journal of Social Structure Journal of Mathematical Sociology Social Network Analysis and Mining SNAM INSNA Connections Journal Connections Bulletin of the International Network for Social Network Analysis Toronto International Network for Social Network Analysis ISSN 0226 1766 Archived from the original on 2013 07 18 Textbooks and educational resources Edit Networks Crowds and Markets 2010 by D Easley amp J Kleinberg Introduction to Social Networks Methods 2005 by R Hanneman amp M Riddle Social Network Analysis Instructional Web Site by S Borgatti Guide for virtual social networks for public administrations 2015 by Mauro D Rios in Spanish Data sets Edit Wikimedia Commons has media related to Social networks Pajek s list of lists of datasets UC Irvine Network Data Repository Stanford Large Network Dataset Collection M E J Newman datasets Pajek datasets Gephi datasets KONECT Koblenz network collection RSiena datasets Retrieved from https en wikipedia org w index php title Social network amp oldid 1050850882, wikipedia, wiki, book,

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