Sociological theory of diffusion

Sociological theory of diffusion

From Wikipedia, the free encyclopedia

The sociological theory of diffusion is the study of the diffusion of innovations throughout social groups and organizations. The topic has seen rapid growth since the 1990s, reflecting curiosity about the process of social change and "fueled by interest in institutional arguments and in network and dynamic analysis."[1] The theory uses a case study of the growth of business computing to explain different mechanisms of diffusion.

The concept of diffusion

In the 1962 book, Diffusion of Innovations, Everett Rogers defines sociological diffusion of innovation as a process in a social system where an innovative idea or concept is spread by members of the social group through certain channels. He identifies four elements that influence how and how quickly a new idea spreads:[2]

• The innovation itself

• The types of communication channels used

• The amount of time the social group is exposed to the innovation

• The nature of the social group

In a study by Surry and Farquhar, researchers explain that the theory of diffusion is used in occupations ranging from marketing to agriculture in order to ensure that new products, ideas, and techniques are well adopted by the social group.[3] The concept of diffusion is of particular interest in the marketing field, as this concept affects the success or failure of new ads or products. Understanding this theory helps marketers influence the way the public will perceive each innovation.

The speed at which an innovation spreads through a mass of people depends on how favorably an idea is perceived by the audience. Innovations that are ill matched with existing techniques are not as well accepted and diffused through the group. Social structures are naturally designed in a hierarchy[citation needed]; thus, different ideas follow different routes or courses in the hierarchy, depending on the type and source of an innovation.[4]

The study of the diffusion of innovations has led to advancements in awareness of three important aspects of social change: the qualities of an innovation which lead to successful diffusion, the effect of peer networking and conversations when it comes to spreading ideas, and the importance of various "user segments" (Robinson). The theory of diffusion of innovations differs from other theories about the processes of change since most changes are improvements, or "reinventions", of a previously existing product or technique. These changes are generally favorably perceived by the members of the group because they usually are more in line with the values and needs of the group.

There are five important qualities that factor into the success or failure of innovations. First, the relative advantage; that is, whether the new innovation surpasses similar existing ideas in terms of satisfaction and convenience. Second, the compatibility of the new idea with the needs and practices of the group members. Third, the simplicity of the innovation: usually, the simpler the innovation, the more quickly the concept is adopted. Fourth, the "trialability"[citation needed] of an innovation; that is, whether it can be tested without commitment for a period of time. An innovation that has an available trial period provides less uncertainty to the group member who will be trying it. Lastly, whether there are observable results with use of the innovation. The more positive and visible results, the higher the likelihood it gets adopted as a permanent idea for the group.[5]

Why diffusion happens

Sociological diffusion occurs when a social group or organization develops an innovation: a new idea or behavior. Diffusion, in the context of corporations and businesses, is a way for an idea to be fleshed out. The diffusion of innovations provides insights into the process of social change: one can observe the qualities that make an innovation successfully spread and the importance of communication and networks.[5] According to Rogers, a new idea is diffused through a decision-making process with five steps:[2]

• Knowledge - An individual first becomes aware of the new innovation, but lacks information and inspiration

• Persuasion - The individual's interest in the innovation spikes, and he or she begins research

• Decision - The individual weighs the positive and negative results of changing to the new idea

• Implementation - The individual adds the innovation into the system. At this stage, he or she also begins to determine the innovation's usefulness.

• Confirmation - The individual decides to continue with the new innovation.

The key part of the five stages is the decision; this is the main reason why diffusion exists. The decision to either adopt or reject the idea is vitally important. Those responsible for evaluating innovations either determine that the new concept is likely to provide future success, and adopt it, or determine that it is likely to be a failure, and continue to move forward in search of other ideas. It is counterproductive for an organization to invest time, energy, and in most cases money, into a poorly developed or bad idea.

An important aspect of the diffusion and decision process is communication. As an idea further develops and spreads, it flows and moves through an organization by communication. Communication is a necessary condition for an idea to take hold.[6] The innovation depends on a communication network within the organization in order to take root. In Emanuel Rosen's book The Anatomy of Buzz, Rosen points out the importance of communication networks in the spread and development of an idea within an organizational system. (Dobson)

Studies of the diffusion of innovation have shown that new ideas must fit with already established system in order for changes not only to occur, but also to occur easily. (Pinard) An innovation faced with structural or ideological barriers cannot diffuse. On the other hand, if a new idea or innovation has few obstacles and acknowledges places where change is logical, movement to it will occur. (Freeman)

Networks and environment

A firm's interaction with other players, along with its environment and organizational culture, are key in the social theory of diffusion.

The use of networks

The effects of networks and institutional environment on adoption of innovations can be explained using a social network theory model. In such a model, nodes represent agents (e.g. companies or organizations) and ties represent a connection between two entities (e.g. a company-client relationship or competitive relationship). Diffusion occurs when a novel idea, product, or process is implemented by an agent and permeates through these ties to others.[7]

Internal and external diffusion

Diffusion of information and ideas has been categorized into two modes:

Internal diffusion is the spread of information and innovations within a network, flowing within a single adopting population – a given industry or geographical network. Internal diffusion dynamics require that innovative and early adopter firms introduce new ideas into a network, which are then picked up by the majority of firms and laggard firms.[8] DiMaggio and Powell (1983)[9] argue that firms search for the best ideas and practices and mimic new ideas that prove to work. This phenomenon is known as mimetic isomorphism,[9] and ironically may lead to clustering of firm structure and practices.[7] Additionally, firms are often forced to adopt new ideas as they are constantly competing with other firms; that is, firms want to seem modernized and seek legitimacy in implementing innovative practices.

External diffusion refers to the introduction of ideas to a network from outside actors: firms or other agents on the edge of the network. Outside actors include the mass media and "change agents." Mass media can amplify trends and movements that occur in the marketplace, introducing new innovations to network members, exposing "best-practice" ideas, and conveying new principles.[8] Change agents are usually business professionals (such as lawyers, consultants, bankers, or politicians) who spread new practices or aid in promoting new ideas.[7] These individuals often introduce business models, legal strategies, or investment techniques that are picked up by several entities within a network and continue to diffuse. Often, such external diffusion leads to conformity of a set of corporate strategies or structures, a phenomenon DiMaggio and Powell called "normative isomorphism".

Environmental and cultural factors of diffusion

An agent's environmental and cultural makeup influence the decision to adopt an idea diffusing through a network. Some of the major characteristics of firms that influence their decision to innovate are clustering, weak ties, and firm size.

Clustering', the existence of a group of tightly connected agents, is a frequent concept in network theory.[10] It includes, for example, similar firms locating themselves in close proximity to each other (Silicon Valley for technology firms; New York for banking services). Such clustering and close proximity increases the diffusion rate of ideas for firms within a cluster, as other firms are more likely to adopt an idea if another firm has adopted it within its cluster.[7]

An agent with weak ties has a connection to two or more clusters.[11] These agents are integral in connecting groups, as they provide communication between large clusters. Firms with weak ties can be isolated firms, firms with business in two or more spaces, or those which are external change agents. Firms with weak ties introduce clusters to new, proven methods.

Firm size has been shown to have an influence on the rate of diffusion. Strang and Soule (1998) have shown that large, technical, and specialized organizations with informal cultures tend to innovate much faster than other firms. Smaller and more rigid firms attempt to mimic these "early adopters" in attempt to keep up with competition.[7]

Mathematical treatment

Mathematical models can be used to study the spread of technological innovations among individuals connected to each other by a network of peer-to-peer influences, such as in a physical community or neighborhood.[12]

Complex system (particularly complex network) models can be used to represent a system of individuals as nodes in a network (or Graph (discrete mathematics)). The interactions that link these individuals are represented by the edges of the network and can be based on the probability or strength of social connections. In the dynamics of such models, each node is assigned a current state, indicating whether or not the individual has adopted the innovation, and model equations are used to describe the evolution of these states over time.[13]

In threshold models[14] the uptake of technologies is determined by the balance of two factors: the (perceived) usefulness (sometimes called utility) of the innovation to the individual as well as barriers to adoption, such as cost. The multiple parameters that influence decisions to adopt, both individual and socially motivated, can be represented by such mathematical models.

Computer models have been developed to investigate the balance between the social aspects of diffusion and perceived intrinsic benefit to the individuals.[15] When the effect of each individual node was analyzed along with its influence over the entire network, the expected level of adoption was seen to depend on the number of initial adopters and the structure and properties of the network. Two factors in particular emerged as important to successful spread of the innovation: The number of connections of nodes with their neighbors, and the presence of a high degree of common connections in the network (quantified by the clustering coefficient).

Case study: Diffusion of business computing in organizations

To illustrate how different diffusion mechanisms can have varying effects in individual cases, consider the example of business computing. The 1980s and 1990s saw a rapid paradigm shift in the way many organizations operated; specifically, the rise of computers and related technologies saw organizations adopt these innovations to help run their business (Attewell 1992:1[16]). Thus, the diffusion of business computing through organizations during this time period provides an informative case study through which to examine different mechanisms of diffusion and their respective roles.

Networks

The roles of communication networks, as described by traditional theories of diffusion, have been to facilitate information flow about a new innovation and thus remove one of the major barriers to adoption. In this model, those closest to the initial champions of a new innovation are quicker to respond and adopt, while those farther away will take more time to respond (Rogers 1983;[8] Strang and Soule 1998:272[17]). This theory about the roles of networks in diffusion, while widely applicable, requires modification in this particular case, among others. Attewell (1992)[16] argues that in this case, knowledge of the existence of computers and their business applications far preceded their eventual adoption. The main barrier to adoption was not awareness, but technical knowledge: knowledge of how to effectively integrate computing into the workplace. Thus, the most relevant networks to the diffusion of business computing were those networks that transmitted the technical knowledge required to utilize the innovation, not those that simply transmitted awareness of the idea behind the innovation.

Institutions

New institutions, in particular those which acted as educators or consultants, also played an important role in the diffusion of business computing. In order to adapt to evolving trends in business computing, organizations first needed to gain the technical knowledge necessary to operate the technology (Attewell 1992:3-6).[16] The "knowledge barrier" could be reduced or partially circumvented, however, by the formation of new institutions. The new institutions that formed during this time period – such as service bureaus, consultants, and companies creating simplifications of the technology – lowered the knowledge barrier and allow for more rapid diffusion of the ideas and technology behind business computing. This explains the phenomenon in which, at first, many organizations obtained business computing as an out-sourced service. However, after these service institutions effectively lowered the barrier to adoption, many organizations became capable of bringing business computing in-house (Attewell 1992:7-8).[16]

Innovation decisions

Rogers (1983)[8] notes two important ways in which innovations are adopted by organizations: collective innovation decisions, and authority innovation decisions. "Collective innovation decisions" are best defined as a decision that occurs as the result of a broad consensus for change within an organization. "Authority innovation decisions", on the other hand, need only the consensus of a few individuals with large amounts of power within the organization. In the case of organizations adopting business computing, authority decisions were largely impossible. As J.D. Eveland and L. Tornatzky (1990)[18] explain, when dealing with advanced technical systems such as those involved with business computing, “decisions are often many (and reversed), and technologies are often too big and complex to be grasped by a single person's cognitive power – or usually, to be acquired or deployed within the discretionary authority of any single organizational participant." Therefore, a much broader consensus within an organization was required to reach the critical mass of technical knowledge and authority necessary to adapt to business computing. This provided an opportunity for collective innovation decisions within the organization.

Evolutionary Biology & Social Network Diffusion

Here are two articles today speaking to the 6 degrees of separation involving real time network orthogonal structures and affective matrices moving towards efficiency for optimal affective innovative diffusion. Its found everywhere in life. Especially in living macro systems.

Practically it shows how social networking might include innovative macro-societal thinking and affective restructuring of behavior in postmodern environments requiring deep positive change vs post-truth societies refusing learning or change in the face of new global realities. This latter movement seems to be excessively pendantic or, marooned in slavish attention to doctrinaire traditions, without willingness to expand or explore contemporary non-traditional systems requiring a new set of orthogonal societal fundamentals.

Here then is described how social innovative thinking can be a boon to societies stuck within their older, inherited, identities requiring updating, renovation and liberation.

R.E. Slater

October 21, 2017

Social network diagram. Credit: Daniel Tenerife/Wikipedia

Six degrees of separation: Why it is a small world after all

https://m.phys.org/news/2017-10-degrees-small-world.html

October 19, 2017

It's a small world after all - and now science has explained why. A study conducted by the University of Leicester and KU Leuven, Belgium, examined how small worlds emerge spontaneously in all kinds of networks, including neuronal and social networks, giving rise to the well-known phenomenon of "six degrees of separation".

Many systems show complex structures, of which a distinctive feature is small-world network organization. They arise in society as well as ecological and protein networks, the networks of the mammalian brain, and even human-built networks such as the Boston subway and the World Wide Web.

The researchers set out to examine whether this is a coincidence that such structures are so wide-spread or is there a common mechanism driving their emergence?

A study recently published in Scientific Reports by an international team of academics from the University of Leicester and KU Leuven showed that these remarkable structures are reached and maintained by the network diffusion, i.e. the traffic flow or information transfer occurring on the network.

The research presents a solution to the long-standing question of why the vast majority of networks around us (WWW, brain, roads, power grid infrastructure) might have a peculiar yet common structure: small-world topology.

The study showed that these structures emerge naturally in systems in which the information flow is accounted for in their evolution.

Nicholas Jarman, who recently completed his PhD degree at the Department of Mathematics, and is first author of the study, said: "Algorithms that lead to small-world networks have been known in scientific community for many decades. The Watts-Strogatz algorithm is a good example. The Watts-Strogatz algorithm, however, was never meant to address the problem of how small-world structure emerges through self-organisation. The algorithm just modifies a network that is already highly organised."

Professor Cees van Leeuwen, who led the research at KU Leuven said:

"The network diffusion steers network evolution towards emergence of complex network structures. The emergence is effectuated through adaptive rewiring: progressive adaptation of structure to use, creating short-cuts where network diffusion is intensive while annihilating underused connections. The product of diffusion and adaptive rewiring is universally a small-world structure. The overall diffusion rate controls the system's adaptation, biasing local or global connectivity patterns, the latter providing a preferential attachment regime to adaptive rewiring. The resulting small-world structures shift accordingly between decentralised (modular) and centralised ones. At their critical transition, network structure is hierarchical, balancing modularity and centrality - a characteristic feature found in, for instance, the human brain."

Dr Ivan Tyukin from the University of Leicester added: "The fact that diffusion over network graph plays crucial role in keeping the system at a somewhat homeostatic equilibrium is particularly interesting. Here we were able to show that it is the diffusion process, however small or big gives rise to small-world network configurations that remain in this peculiar state over long intervals of time. At least as long as we were able to monitor the network development and continuous evolution".

Alexander Gorban, Professor in Applied Mathematics, University of Leicester commented:

"Small-world networks, in which most nodes are not neighbours of one another, but most nodes can be reached from every other node by a small number of steps, were described in mathematics and discovered in nature and human society long ago, in the middle of the previous century. The question, how these networks are developing by nature and society remained not completely solved despite of many efforts applied during last twenty years. The work of N. Jarman with co-authors discovers a new and realistic mechanism of emergence of such networks. The answer to the old question became much clearer! I am glad that the University of Leicester is a part of this exciting research."

* * * * * * * * * *

Social network

From Wikipedia, the free encyclopedia

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).

It has been suggested that Social graph be merged into this article. (Discuss) Proposed since February 2016.

Sociology

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 interactionsbetween 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 socialpsychology, 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]

Overview

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^[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

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").^[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 Lévi-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-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.

Levels of analysis

Self-organization of a network, based on Nagler, Levina, & 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

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

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 workgroup 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 coefficientdistribution, which decreases as the node degree increases. This distribution also follows a power law.^[44] 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 transferinteractions 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

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.^[46]

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".^[47]

Structural holes

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.^[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

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.

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.

Criminal networks

In criminology and urban sociology, much attention has been paid to the social networks among criminal actors. For example, Andrew Papachristos^[50] has studied gang murders 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.

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.^[51]

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.^[52]

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.^[53][54]

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.

Literary networks

In the study of literary systems, network analysis has been applied by Anheier, Gerhards and Romo,^[55] De Nooy,^[56] and Senekal,^[57] 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.^[58] Intra-organizational networks have been found to affect organizational commitment,^[59] organizational identification,^[37] interpersonal citizenship behaviour.^[60]

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 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). Studies show that a positive relationship exists between social capital and the intensity of social network use.^[61][62]

Mobility 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.^[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."^[63] 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 3 consulting firm consultants and mid-size industry firms.^[64] 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.^[65] 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

Main article: Social media

Computer networks combined with social networking software produces 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.^[66] 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.^[67]