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Community Discovery: Simple and Scalable Approaches

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User Community Discovery

Part of the book series: Human–Computer Interaction Series ((HCIS))

Abstract

The increasing size and complexity of online social networks have brought distinct challenges to the task of community discovery. A community discovery algorithm needs to be efficient, not taking a prohibitive amount of time to finish. The algorithm should also be scalable, capable of handling large networks containing billions of edges or even more. Furthermore, a community discovery algorithm should be effective in that it produces community assignments of high quality. In this chapter, we present a selection of algorithms that follow simple design principles, and have proven highly effective and efficient according to extensive empirical evaluations. We start by discussing a generic approach of community discovery by combining multilevel graph contraction with core clustering algorithms. Next we describe the usage of network sampling in community discovery, where the goal is to reduce the number of nodes and/or edges while retaining the network’s underlying community structure. Finally, we review research efforts that leverage various parallel and distributed computing paradigms in community discovery, which can facilitate finding communities in tera- and peta-scale networks.

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Notes

  1. 1.

    http://newsroom.fb.com/company-info/. Accessed in December 2014.

  2. 2.

    Here, we will discuss methods based on both shared-memory and distributed-memory architectures.

  3. 3.

    Overlapping community detection has also attracted considerable research attention [51, 53], yet existing studies have not adapted the multilevel framework discussed here. Combining the multilevel paradigm with overlapping community discovery will be an exciting future direction.

  4. 4.

    http://snap.stanford.edu/data/index.html.

  5. 5.

    Note that node sampling can also be achieved by creating an edge-induced subgraph from a subset of edges, therefore the node selection process is not always explicitly performed. The key distinction here is whether all nodes from the original graph are kept in the resultant sample graph.

  6. 6.

    The forest fire model described here is slightly different from that originally proposed in [25], which operates on directed graphs and thus has two parameters to control the “burning” of in- and out-links, respectively.

  7. 7.

    Both content and attribute information are modeled as an auxiliary feature vector associated with each node in the graph, so that the formulation is applicable to text, image, and many other forms of information, all of which will be referred to as “content information” henceforth.

  8. 8.

    An empirical guideline to select K is to let the size of \(E_{content}\) be similar to that of E.

  9. 9.

    This is different from the Twitter network described in Sect. 2.2.5.

  10. 10.

    Although forest fire is designed for node sampling, one can perform forest fire repeatedly, each time on a randomly-selected unburned node, until most nodes are burned. The collection of all burned edges are considered sampled edges.

  11. 11.

    The computed independent set is no longer guaranteed to be maximal.

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Acknowledgments

We are thankful to the Editors and anonymous reviewers for their valuable comments, insightful suggestions and constructive feedback that greatly helped improving this article.

This work is supported by NSF Grants IIS-1111118, CCF-1240651, and DMS-1418265. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

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Correspondence to Yiye Ruan .

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Ruan, Y., Fuhry, D., Liang, J., Wang, Y., Parthasarathy, S. (2015). Community Discovery: Simple and Scalable Approaches. In: Paliouras, G., Papadopoulos, S., Vogiatzis, D., Kompatsiaris, Y. (eds) User Community Discovery. Human–Computer Interaction Series. Springer, Cham. https://doi.org/10.1007/978-3-319-23835-7_2

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