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3.3 Detecting communities We now turn our attention to the detection of communities in online social networks. A community is a subset of the users in a social network that is more tightly in- terconnected than the overall network [119]. Thus, all of the work described in this section tries to detect densely connected components of graphs. At a high level, the approaches can be divided into global approaches, which assume knowledge of the entire graph, or local approaches, which only assume detailed knowledge of a region of the network. After briefly describing how communities were detected classically in sociology, we describe the global and local approaches. Then, we describe empirical studies of social networks that have looked for the presence of communities. 3.3.1 Classical community detection Classical community detection in sociology took the approach of partitioning the ver- tices in a social network into different communities while minimizing the number of edges between communities. Within this approach, there are two main algorithms: spectral bisection [130] and the Kernighan-Lin algorithm [73]. Both algorithms par- tition the graph into the best two communities possible, and then further subdivide those two until reaching the user-specified number of communities. However, both algorithms require the user to specify the sizes of the two communities initially, as well as the final number of communities desired. 41PDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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