Social network analysis

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A drawing of a "social graph" where each person is represented by a dot called a node and the friendship relationship is represented by a line called edge
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Last Update

  • Updated.jpg This entry is out of date, and will not be updated, June 2017

Introduction

See also Citation analysis | Data management | Data visualization | Digital Communities of Practice (CoPs) | Semantic search

"...power no longer resides exclusively (if at all) in states, institutions, or large corporations. It is located in the networks that structure society. Social network analysis seeks to understand networks and their participants and has two main focuses: the actors and the relationships between them in a specific social context..."

Social networks can be described as "...a web of social relationships that surrounds individuals” (Heaney & Israel, 2002, p. 185). Social networks are social structures comprised of social actors (individuals and organizations) and single or dyadic ties between them. The social network perspective provides methods for analyzing the structure of entire social entities as well as various theories explaining the patterns observed in these structures. According to Hall & Wellman (1985), a social network is "a set of nodes ...tied by one or more specific types of relations between them" (p. 25). Nodes, in social network research, refer to individuals and the ties they represent in a flow of resources from one person to another (mediated through casual acquaintances and close bonds). By extension, social networks might be best described as an analysis of the ecosystem that makes up the entirety of social interaction between "linked" people, intellectual property and information.

Social network analysis allows researchers to describe, integrate and analyze spatial, mathematical and substantive dimensions of social structures that are formed as ties between people, groups and other social nodes. Researchers in the area represent networks graphically, locate them spatially, and describe their properties mathematically. These spatial and mathematical relationships (i.e., “networks”) are related to the content and quality of interpersonal ties, individual and group types and behaviours, and the well-being and dynamics of groups and communities. SNA is being used to locate meaningful measurements of social integration in studies where individual outcomes are situated around community functions and population health. SNA can be used to study the transmission of viral infections, behaviours, attitudes, information or the diffusion of medical practices.

Resources (i.e., support) that move through social networks vary in quality (dimensions include informational or emotional), quantity, (times support flows between two specific members), multiplexity (how many different dimensions of support flow through links), and reciprocity (whether support flows in both directions or just one). Social network analysis also uses sociograms to visually depict the structure of networks and the flow of resources from one entity to another. Social network analysis has been described as visually representing social ties in a community of practice or akin to an "organizational x-ray".

Coursera course

  • social network analysis, its theory and computational tools, to make sense of social and information networks fueled by the internet
  • Instructor: Lada Adamic blog

Tweet visualization

  • online social networking platforms make it possible to study communication networks on an unprecedented scale; digital trace data can be compiled into large data sets of online discourse; it is a challenge to collect, store, filter, and analyze large amounts of data, even by experts in the computational sciences; describes recent extensions to Truthy (http://truthy.indiana.edu/about), a system that collects Twitter data to analyze discourse in near real-time; several interactive visualizations and analytical tools included with the goal of enabling citizens, journalists, and researchers to understand and study online social networks

Research

The purpose of this article is to identify the bibliometric characteristics of research librarianship literature and to visualize relationships in research librarianship by means of social network analysis. It was found that the majority (66%) of articles had single authorship and College & Research Libraries is the prominent journal in research librarianship. Peter Hernon is the most productive and cited author. The findings can be used by the research librarianship community to better understand the core literature.

Social network analysis is a way to study the interactions and exchange of resources among people in a network. It provides insight into structural and behavioral complexities that influence capacity building in evidence-informed decision making. An analysis was conducted to understand if and how the staff of a public health department in Ontario turn to peers to get help incorporating research evidence into practice. The staff responded to an online questionnaire about information seeking, identification of expertise and friendship. Three networks were developed based on the 170 participants. Overall shape, key indices, the most central people and brokers, and their characteristics were identified. The analysis shows low density and a localized information-seeking network. Interpersonal connections were clustered by organizational divisions; people limit their information-seeking connections to a handful of peers in their area. Recognition of expertise and friendship shows more cross-divisional connections. Members of the Medical Officer of Health were located at the heart of the network bridging divisions. A group of consultants and managers were the most-central in the network, connecting their divisions to the center of the network. In each division, there were locally central staff, mainly practitioners, who connected their neighboring peers; but were not necessarily connected to other experts or managers. SNA is useful in providing a systems approach to understand how knowledge flows in organizations. The findings of this study can be used to identify early adopters of knowledge translation interventions, forming Communities of Practice, and potential internal knowledge brokers.

Important concepts

  • Betweenness is the extent to which a node lies between other nodes in a network; this measure takes into account the connectivity of a node's neighbors, giving higher value for nodes which bridge clusters; the measure reflects the number of people who a user is connecting indirectly through their direct links.
  • Bridge – an edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph
  • Centrality gives an idea of the social power of a node based on how well they "connect" the network; "betweenness", "closeness" and "degree" are also measures of centrality
  • homophily (ie., "love of the same") is a tendency of individuals to associate and bond with similar people; the presence of homophily has been revealed in several network studies.
  • node is a unit in a network that can be tied to another node; a group of nodes are members in a social network; the nodes an individual is connected to are the social contacts of that individual.
  • social capital refers to the social networks, systems of reciprocal relations, sets of norms, or levels of trust individuals and groups have (or resources arising from them). Its popularity can be traced to three authors - Pierre Bourdieu (1930-2002), James Coleman and Robert Putnam - each of whom has a distinct conception of social capital.
  • social network is a set of socially-relevant nodes connected by one or more relationships. Nodes, or members in the network, are the units that are connected by the relations and patterns studied. These are commonly people and organizations but can be other units studied as nodes such as web pages (Watts, 1999), journal articles (White, Wellman and Nazer, 2004), countries, neighbourhoods, departments in organizations (Quan-Haase and Wellman, 2006) or professions (Boorman and White, 1976; White et al., 1976).
  • social network analysis views social relationships in terms of network theory consisting of nodes and ties (also called edges, links, or connections). Nodes are the individual actors within the networks, and ties are the relationships between the actors. The resulting graph-based structures are often very complex. There can be many kinds of ties between the nodes. Research in a number of academic fields has shown that social networks operate on many levels, from families up to the level of nations, and play a critical role in determining the way problems are solved, organizations are run, and the degree to which individuals succeed in achieving their goals.
  • sociograms is a graphic representation of social links that someone enjoys either in their relationships online or offline. A sociogram, in other words, is a diagram of the structure and patterns of interactions within a group of people. A sociogram can be drawn on the basis of different criteria: social relations, channels of influence, lines of communication etc.

Applications in science

Social network analysis has been used in epidemiology to help understand how patterns of human contact aid or inhibit the spread of diseases such as HIV. The evolution of social networks can sometimes be depicted as a model which may provide insight into the interplay between communication rules, information-sharing and social norms. SNA may also be effective in mass surveillance or to develop mass communication strategies. Diffusion of innovations theory explores social networks and their role in influencing the spread of new ideas and practices. Change agents and opinion leaders often play a role in the adoption of innovation although factors inherent to it also play a role.

Dunbar has said that the typical size of an egocentric network is limited to 150 members due to limits in the capacity of human communication channels. The rule arises from cross-cultural studies in sociology and especially anthropology of the maximum size of a village (in modern parlance most reasonably understood as an ecovillage). In evolutionary psychology, the number may be some kind of limit of average human ability to recognize members and track emotional facts about members in a group. However, it may be due to economics and the need to track "free riders" as it may be easier in larger groups to take advantage of the benefits of living in a community without contributing to those benefits.

References

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