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Social network analysis: An overview

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Social network analysis (SNA) is a core pursuit of analyzing social networks today. In addition to the usual statistical techniques of data analysis, these networks are investigated using SNA measures. It helps in understanding the dependencies between social entities in the data, characterizing their behaviors and their effect on the network as a whole and over time. Therefore, this article attempts to provide a succinct overview of SNA in diverse topological networks (static, temporal, and evolving networks) and perspective (ego‐networks). As one of the primary applicability of SNA is in networked data mining, we provide a brief overview of network mining models as well; by this, we present the readers with a concise guided tour from analysis to mining of networks. This article is categorized under: Application Areas > Science and Technology Technologies > Machine Learning Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Commercial, Legal, and Ethical Issues > Social Considerations
An example network representation
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Temporal networks represented as (a) contact sequence network and (b) interval graph
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An example network with three distinct communities: C1 = {A,B,C,D,E,F}, C2 = {G,H,I}, C3 = {J,K,L,M,N,O,P}
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Illustration of the process behind Pagerank algorithm in a network comprised of six nodes. The first network (a) corresponds to the initialization step. In network (b) are shown the updated Pagerank values at the end of the first iteration of the algorithm. Note that node D is so far the most authoritative node, with a Pagerank value of . The rightmost network (c) corresponds to the second (and last) iteration of Pagerank. Here, we notice that node B overtakes the position of node D in terms of Pagerank values
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Structurally different ego‐networks for demonstration
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A directed and unweighted graph G represented by means of an adjacency matrix (left‐side of the figure) and an adjacency list (right‐side of the figure)
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Commercial, Legal, and Ethical Issues > Social Considerations
Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction
Technologies > Machine Learning
Application Areas > Science and Technology

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