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Fast Fragmentation of Networks Using Module-Based Attacks.

Bruno Requião da Cunha1,2, Juan Carlos González-Avella1,3, Sebastián Gonçalves1

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We present a new module-based method to efficiently break complex networks by targeting inter-community links. This approach outperforms traditional methods, significantly fragmenting networks like the US power grid.

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Area of Science:

  • Network Science
  • Complex Systems Analysis
  • Computational Social Science

Background:

  • Efficiently breaking complex networks is crucial for practical applications.
  • Existing methods often rely on node degree or betweenness centrality, with varying success.
  • Understanding network modularity is key to improving fragmentation strategies.

Purpose of the Study:

  • To introduce a novel module-based method for efficient complex network fragmentation.
  • To demonstrate the method's effectiveness across diverse network types (social, infrastructure, biological).
  • To compare the module-based approach against traditional targeted attack strategies.

Main Methods:

  • Identifying topological communities using a heuristic community finding algorithm.
  • Removing nodes participating in inter-community links, ordered by betweenness centrality.
  • Applying the method to social, infrastructure, and biological network examples.

Main Results:

  • The module-based method consistently outperforms node degree and betweenness centrality attacks.
  • Fragmentation efficiency is strongly correlated with the network's modularity.
  • In the US power grid, removing only 3% of nodes resulted in fragments 20 times smaller than betweenness-based attacks.

Conclusions:

  • The module-based approach offers a more efficient strategy for complex network fragmentation.
  • Network modularity is a critical factor influencing the success of fragmentation.
  • This method provides a significant advancement for network analysis and disruption.