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An efficient multilevel scheme for coarsening large scale social networks

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Abstract

The explosive growth of data raised from social networks, hinders researchers from analysing them in a good way. So, is it possible to rapidly “zoom-out” from this huge network while preserving its whole structure? In fact, this technique is named “graph’s reduction” and represents a significant task in social networks’ analysis. Thus, several methods have been developed to pull a smaller succinct version of the graph. Some of them belong to the category of “graph sampling” and risk losing key characteristics of communities. Others are part of “coarsening strategy” and designed to cope with the problem of community discovering, which is our desired purpose. In this paper, we propose a multi-level coarsening algorithm called MCCA (Multi-level Coarsening Compact Areas). The main strategy of this algorithm is to merge well connected zones in every level by updating edge and vertex weight until a stopping criterion is met. Using real-world social networks, we evaluate the quality and scalability of MCCA. Furthermore, we compared it with eight known proposals. We also show how our method can be used as a preliminary step for community detection without much loss of information.

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Notes

  1. http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/

  2. https://gephi.org/users/download/

  3. http://glaros.dtc.umn.edu/gkhome/metis/metis/download

  4. https://github.com/dlasalle/nerstrand

  5. http://www.cs.vt.edu/badityap/CODE/coarsenet.tgz

  6. http://algo2.iti.kit.edu/kahip/

  7. https://github.com/chronotable/coarsening-infnet

  8. http://www.eng.usf.edu/chakeri/SpectralSparsify.m

  9. http://www.cs.cmu.edu/jkoutis/SpectralAlgorithms.htm

  10. Code at: http://www.ludowaltman.nl/slm/

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Acknowledgements

We would like to thank Dominique LaSalle, Naoto Ohsaka, Roland Glantz and Alireza Chakeri for their fruitful discussions about their proposed models as well as providing their source codes used in our simulations (respectively: Nerstrand, MaxInf, TREE and SPER). We also thank the anonymous reviewers for their valuable remarks which led to a substantial improvement in the quality of the paper.

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  1. MARS Research Lab LR17ES05 Higher Institute of Computer Science and Telecom (ISITCom), University of Sousse, Sousse, Tunisia

    Delel Rhouma & Lotfi Ben Romdhane

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  1. Delel Rhouma
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  2. Lotfi Ben Romdhane
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Correspondence to Delel Rhouma.

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Rhouma, D., Ben Romdhane, L. An efficient multilevel scheme for coarsening large scale social networks. Appl Intell 48, 3557–3576 (2018). https://doi.org/10.1007/s10489-018-1162-x

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