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Quantifying Node-Based Core Resilience

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Machine Learning and Knowledge Discovery in Databases: Research Track (ECML PKDD 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14171))

Abstract

Core decomposition is an efficient building block for various graph analysis tasks such as dense subgraph discovery and identifying influential nodes. One crucial weakness of the core decomposition is its sensitivity to changes in the graph: inserting or removing a few edges can drastically change the core structure of a graph. Hence, it is essential to characterize, quantify, and, if possible, improve the resilience of the core structure of a given graph in global and local levels. Previous works mostly considered the core resilience of the entire graph or important subgraphs in it. In this work, we study node-based core resilience measures upon edge removals and insertions. We first show that a previously proposed measure, Core Strength, does not correctly capture the core resilience of a node upon edge removals. Next, we introduce the concept of dependency graph to capture the impact of neighbor nodes (for edge removal) and probable future neighbor nodes (for edge insertion) on the core number of a given node. Accordingly, we define Removal Strength and Insertion Strength measures to capture the resilience of an individual node upon removing and inserting an edge, respectively. As naive computation of those measures is costly, we provide efficient heuristics built on key observations about the core structure. We consider two key applications, finding critical edges and identifying influential spreaders, to demonstrate the usefulness of our new measures on various real-world networks and against several baselines. We also show that our heuristic algorithms are more efficient than the naive approaches.

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Notes

  1. 1.

    http://snap.stanford.edu/.

  2. 2.

    http://networkrepository.com/.

  3. 3.

    https://github.com/erdemUB/ECMLPKDD23.

  4. 4.

    https://ubir.buffalo.edu/xmlui/handle/10477/79221.

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Acknowledgments

Hossain and Sarıyüce were supported by NSF Award #1910063 and used resources of the Center for Computational Research at the University at Buffalo. Soundarajan was supported by NSF Award #1908048.

Author information

Authors and Affiliations

  1. University at Buffalo, Buffalo, NY, 14260, USA

    Jakir Hossain & Ahmet Erdem Sarıyüce

  2. Syracuse University, Syracuse, NY, 13244, USA

    Sucheta Soundarajan

Authors
  1. Jakir Hossain
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  2. Sucheta Soundarajan
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  3. Ahmet Erdem Sarıyüce
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Corresponding author

Correspondence to Jakir Hossain .

Editor information

Editors and Affiliations

  1. University of Michigan, Ann Arbor, MI, USA

    Danai Koutra

  2. University of Vienna, Vienna, Austria

    Claudia Plant

  3. Max Planck Institute for Software Systems, Kaiserslautern, Germany

    Manuel Gomez Rodriguez

  4. Politecnico di Torino, Turin, Italy

    Elena Baralis

  5. CENTAI, Turin, Italy

    Francesco Bonchi

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Ethical Statement

Our contribution is algorithmic in nature, building on previously proposed concepts. We work on public datasets. We do not foresee any ethical implications of our work.

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Hossain, J., Soundarajan, S., Sarıyüce, A.E. (2023). Quantifying Node-Based Core Resilience. In: Koutra, D., Plant, C., Gomez Rodriguez, M., Baralis, E., Bonchi, F. (eds) Machine Learning and Knowledge Discovery in Databases: Research Track. ECML PKDD 2023. Lecture Notes in Computer Science(), vol 14171. Springer, Cham. https://doi.org/10.1007/978-3-031-43418-1_16

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  • DOI: https://doi.org/10.1007/978-3-031-43418-1_16

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