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Spectral Measures of Distortion for Change Detection in Dynamic Graphs

  • Luca Castelli Aleardi
  • Semih Salihoglu
  • Gurprit Singh
  • Maks Ovsjanikov
Conference paper
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 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Luca Castelli Aleardi
    • 1
  • Semih Salihoglu
    • 2
  • Gurprit Singh
    • 3
  • Maks Ovsjanikov
    • 1
  1. 1.LIX, Ecole PolytechniquePalaiseauFrance
  2. 2.University of WaterlooWaterlooCanada
  3. 3.Max Planck Institute for InformaticsSaarbrückenGermany

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