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WIREs Data Mining Knowl Discov
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An overview of social network analysis

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Data mining is being increasingly applied to social networks. Two relevant reasons are the growing availability of large volumes of relational data, boosted by the proliferation of social media web sites, and the intuition that an individual's connections can yield richer information than his/her isolate attributes. This synergistic combination can show to be germane to a variety of applications such as churn prediction, fraud detection and marketing campaigns. This paper attempts to provide a general and succinct overview of the essentials of social network analysis for those interested in taking a first look at this area and oriented to use data mining in social networks. © 2012 Wiley Periodicals, Inc.

Figure 1.

A directed graph D represented by means of an adjacency matrix (left‐hand side of the figure) and an adjacency list (right‐hand side of the figure).

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Figure 2.

Illustration of the process behind Pagerank algorithm in a network comprised 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 5/12.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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Figure 3.

Illustration of a network with three distinct communities: C1 = {A, B, C, D, E, F},  C2 = {G, H, I},  and   C3 = {J, K, L, M, N, O, P}.

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