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Kernel spectral clustering with memory effect

Author

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  • Langone, Rocco
  • Alzate, Carlos
  • Suykens, Johan A.K.

Abstract

Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.

Suggested Citation

  • Langone, Rocco & Alzate, Carlos & Suykens, Johan A.K., 2013. "Kernel spectral clustering with memory effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(10), pages 2588-2606.
    • Handle: RePEc:eee:phsmap:v:392:y:2013:i:10:p:2588-2606
    DOI: 10.1016/j.physa.2013.01.058
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    References listed on IDEAS

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    1. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
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    Cited by:

    1. Peter Sarlin, 2016. "Visual Macroprudential Surveillance of Banks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(4), pages 257-264, October.

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