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Community Detection Using Synthetic Coordinates and Flow Propagation

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Emergent Computation

Part of the book series: Emergence, Complexity and Computation ((ECC,volume 24))

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Abstract

Various applications like finding web communities, detecting 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, based on local interactions between neighboring nodes and on an unsupervised distributed hierarchical clustering algorithm. In this paper, we describe two novel community detection algorithms, one for full graph communities detection and one for single community detection. The novelty of the first proposed approach, named SCCD (to stand for Synthetic Coordinate Community Detection), is the fact that the algorithm is based on the use of Vivaldi synthetic network coordinates computed by a distributed algorithm . We also present an extended version of said algorithm, modified to deal efficiently with community detection on dynamic graphs . Finally, we present a new algorithm which partially analyzes a graph to detect the community of a single node. The current paper not only presents two efficient community finding algorithms, but also demonstrates that synthetic network coordinates could be used to derive efficient solutions to a variety of problems. Experimental results and comparisons with other methods from the literature are presented for a variety of benchmark graphs with known community structure, derived by varying a number of graph parameters and real dataset graphs. The experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme.

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Notes

  1. 1.

    http://www.csd.uoc.gr/~cpanag/DEMOS/commDetection.htm.

  2. 2.

    Connections with nodes that have high stored flow.

  3. 3.

    In order to speed up the algorithm, we search for the global minimum in the range \([|n(s)|,\frac{|\{x \in V : S(x) > 0\} |}{2}]\).

  4. 4.

    goo.gl/867M4z.

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Acknowledgments

This research has been partially co-financed by the European Union (European Social Fund—ESF) and Greek national funds through the Operational Program “Education and Lifelong Learning” of the National Strategic Reference Framework (NSRF)—Research Funding Programs: ARCHIMEDE III-TEI-Crete-P2PCOORD.

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Authors and Affiliations

  1. Department of Informatics Engineering, Technological Educational Institute of Crete, 71004, Heraklion, Crete, Greece

    Paraskevi Fragopoulou & Harris Papadakis

  2. Foundation for Research and Technology-Hellas, Institute of Computer Science, 70013, Heraklion, Crete, Greece

    Paraskevi Fragopoulou & Costas Panagiotakis

  3. Department of Business Administration, Technological Educational Institute of Crete, 72100, Agios Nikolaos, Crete, Greece

    Costas Panagiotakis

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

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Editors and Affiliations

  1. Unconventional Computing Centre, University of the West of England Unconventional Computing Centre, Bristol, United Kingdom

    Andrew Adamatzky

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Fragopoulou, P., Papadakis, H., Panagiotakis, C. (2017). Community Detection Using Synthetic Coordinates and Flow Propagation. In: Adamatzky, A. (eds) Emergent Computation . Emergence, Complexity and Computation, vol 24. Springer, Cham. https://doi.org/10.1007/978-3-319-46376-6_26

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  • DOI: https://doi.org/10.1007/978-3-319-46376-6_26

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