Social Networks and Analytics

Social Networks and Analytics

Yuehua Zhao, Jin Zhang
Copyright: © 2023 |Pages: 11
DOI: 10.4018/978-1-7998-9220-5.ch152
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Social network analysis is becoming a prominent data analytics method. In the big data era, high volumes of a wide variety of valuable data can be easily generated or collected at a high velocity. By using advanced social network analysis, researchers from a range of disciplines can analyze large-scale datasets to learn about relationships underlying social networks and community structures by analyzing the linkage patterns among individuals. In this article, the authors address the basic concepts of social network and analytics as well as the applications of social network analysis methods in big data analysis.
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Social Network

The notion of social network can be as old as the human species (Knoke & Yang, 2019). In social science, the theory of networks has been adopted to explain social phenomena in a wide variety of disciplines ranging from psychology to economics (Borgatti, Mehra, Brass, & Labianca, 2009). Researchers have realized that the network perspective provides new leverage for answering standard social and behavioral science research questions by defining the political, economic, or social structural environment (Wasserman & Faust, 1994).

With the age of Big Data upon us, power is located in the networks that structure society (Serrat, 2017). Borgatti et al. (2009) regarded social network theory as a gold mine that “provides an answer to a question that has preoccupied social philosophy since the time of Plato, namely, the problem of social order: how autonomous individuals can combine to create enduring, functioning societies” (p. 892).

Social Network Analysis

Of vital significance for the development of methods for the analysis of social network is the fact that the unit of analysis is not the individual, but rather an entity made up of a set of individuals and the linkages among them (Wasserman & Faust, 1994). Special network methods are necessary since the focuses of the analysis are dyads (two actors and their ties), triads (three actors and their ties), or larger systems (subgroups of individuals, or entire networks) (Wasserman & Faust, 1994).

The history of social network analysis can be traced back to the 1930s. By the 1980s, social network analysis had become an established field within the social sciences (Borgatti et al., 2009). About ten years later, social network analysis was applied to a wide range of fields such as physics and biology (Borgatti et al., 2009). To date, social network analysis has been widely employed by a great number of disciplines and has become a multidisciplinary methodology.

To explore the study on social network analysis, we conducted a search using the Web of Science databases on 20 January 2022.The search term “social network analysis” was restricted to the topic field. It resulted in 124,694 publications from 1961 to 2022. The temporal distribution of the retrieved publications is displayed in Figure 1 where the X-axis is the publishing year and the Y-axis is the number of publications related to social network analysis. As we can see from Figure 1, the number of publications regarding social network analysis experienced an apparent surge between 2010 and 2020.

Figure 1.

Number of publications in Web of Science Core Collection related to social network analysis


Key Terms in this Chapter

Network Size: Network size refers to the number of actors in a network.

Degree Centrality: Degree centrality refers to the number of connections incident upon a node.

Connection: Connections refer to the links that connect actors to one another.

Centralization: Centralization refers to the degree to which a network is dominated by a single node.

In-Degree: An actor's in-degree refers to the number of connections that lead to that actor.

Actor: Actors refer to any entities acting in any sort of social environment.

Network: Actors and connections together construct networks.

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