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Spectral Measures of Distortion for Change Detection in Dynamic Graphs

Part of the Studies in Computational Intelligence book series (SCI,volume 813)

Abstract

We propose a novel framework for detecting, quantifying and visualizing changes between two snapshots of a dynamic network. Unlike existing approaches, which can be sensitive to minor and isolated changes, and are often based on heuristics, we show how a theoretically-justified, inherently multi-scale notion of change, or distortion, can be defined and computed using spectral graph-theoretic tools. Our primary observation is that informative, robust and multi-scale measures of change can be obtained by computing a real-valued function (which we call the distortion function) on the nodes of the input graph, via the optimization of a pre-defined distortion energy in a provably optimal way. Based on extensive tests on a wide variety of networks, we demonstrate the ability of our approach to highlight the evolution of the network in an informative and multi-scale manner.

Keywords

  • Network visualization
  • Dynamic networks
  • Spectral methods

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Notes

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    The raw data is available at https://www.eecs.wsu.edu/~yyao/StreamingGraphs.html.

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Acknowledgements

Parts of this work were supported by the Jean Marjoulet chair from Ecole Polytechnique, a Google Focused Research Award, the ERC Starting Grant No. 758800 (EXPROTEA) and the French ANR GATO (ANR-16-CE40-0009-01).

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Correspondence to Luca Castelli Aleardi .

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Castelli Aleardi, L., Salihoglu, S., Singh, G., Ovsjanikov, M. (2019). Spectral Measures of Distortion for Change Detection in Dynamic Graphs. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds) Complex Networks and Their Applications VII. COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol 813. Springer, Cham. https://doi.org/10.1007/978-3-030-05414-4_5

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