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Locating communities on graphs with variations in community sizes

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Abstract

A fundamental problem in networking and computing is community detection. Various applications like finding web communities, uncovering the structure of social networks, or even analyzing a graph’s structure to uncover Internet attacks are just some of the applications for which community detection is important. In this paper, we propose an algorithm that finds the entire community structure of a network, represented by an undirected, unweighted graph, based on local interactions between neighboring nodes and on an unsupervised centralized clustering algorithm. The novelty of the proposed approach is the fact that the algorithm is based on the use of network coordinates computed by a distributed algorithm. Experimental results and comparisons with the Lancichinetti et al. method (Phys. Rev. E 80(5 Pt 2), 056117, 2009; New J. Phys. 11(3), 033015, 2009) are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters. Emphasis is given on benchmark graphs with significant variations in the size of their communities. Further experimental results are presented for two real dataset graphs, namely the Enron, and the Epinions graphs, from SNAP, the Stanford Large Network Dataset Collection. The experimental results demonstrate the high performance of our algorithm in terms of accuracy to detect communities, and its computational efficiency.

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Acknowledgements

This project is implemented through the Operational Program “ARCHIMEDE III: Education and Lifelong Learning” (project P2PCOORD) and is co-financed by the European Union (European Social Fund) and Greek national funds (National Strategic Reference Framework 2007–2013).

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Authors and Affiliations

  1. Department of Applied Informatics and Multimedia, Technological Educational Institute of Crete, 71500, Heraklion, Crete, Greece

    Harris Papadakis & Paraskevi Fragopoulou

  2. Department of Commerce and Marketing, Technological Educational Institute of Crete, 72200, Ierapetra, Crete, Greece

    Costas Panagiotakis

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  1. Harris Papadakis
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  2. Costas Panagiotakis
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  3. Paraskevi Fragopoulou
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Correspondence to Costas Panagiotakis.

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Papadakis, H., Panagiotakis, C. & Fragopoulou, P. Locating communities on graphs with variations in community sizes. J Supercomput 65, 543–561 (2013). https://doi.org/10.1007/s11227-012-0806-6

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