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45 node to exactly one community, even if multiple such structures exist. Finally, local approaches allow for natural decentralization, as the computation can be trivially divided up and distributed. Clauset [31] proposed one of the first local approaches to community detection, which was based on the greedy construction of a community around a source node. The algorithm creates a community by adding vertices one-by-one, choosing the vertex at each step that maximizes the ratio of intra-community edges to inter-community edges for the nodes on the “fringe” of the community. Thus, this algorithm tries to create a strong community by greedily picking nodes that have many links inside the community. Bagrow et al. [14] proposed an alternative algorithm, which adds all of the k-hop vertices at each step, until the ratio of inter-community to intra-community links falls below a threshold. Both of these were shown to detect communities in synthetic graphs, as well as a real-world product recommendation network. Recently, Wakita et al. [155] proposed a modification to the Clauset algorithm, which is capable of identifying communities in social networks with up to 5 million users. However, their work does not provide any validation of the community structure inferred from the network. Additionally, two new local community detection algorithms have been proposed to improve the speed and performance of community detection. Luo et al. [99] pro- posed an algorithm similar to Clauset’s, with the exception that it iteratively adds and removes nodes, continuing until adding or removing any single vertex would notPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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