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44 proaches, which were only concerned with finding the best way to partition the nodes into single, non-overlapping set of communities. The overlapping approaches include work by Palla et al. [123], which used k-cliques to find overlapping communities at different scales. Baumes et al. [16,17] proposed a similar approach for finding over- lapping communities by first looking for dense collections of nodes in the graph. Du et al. [46] presented an algorithm to detect communities in large-scale social networks by considering the overlapping nature of communities. Finally, Li et al. [92] proposed a separate approach for overlapping community detection based on triangle formation and clustering based on text similarity. 3.3.3 Local community detection One potential downside of the global approaches to community detection is that the structure of the entire graph must be known; this is often prohibitively expensive (as many real-world graphs are extremely large) or hard to obtain (for example, the graph of Web pages). As an alternative, a number of researchers have looked at local approaches to detecting communities, which use only local knowledge to build a community around a set of source nodes. In contrast with the global approaches, local approaches have the potential to be significantly more scalable and applicable to much larger graphs, as well as graphs which are not completely visible due to privacy restrictions. Moreover, local approaches to community detection also hold the potential to detect multiple community structures – global approaches assign eachPDF Image | Online Social Networks: Measurement, Analysis, and Applications to Distributed Information Systems
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