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ABCDE: Approximating Betweenness-Centrality ranking with progressive-DropEdge.

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  • 1Information Systems and Computer Engineering, Instituto Superior Técnico, Lisbon, Lisboa, Portugal.

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Summary
This summary is machine-generated.

This study introduces a deep graph convolutional neural network to efficiently approximate the top-k nodes with high betweenness-centrality in large networks. The novel approach significantly speeds up computation and reduces resource usage for network analysis tasks.

Keywords:
Approximation algorithmsBetweenness-centralityGraph centralityGraph convolutional networksGraph neural networksProgressive DropEdge

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Area of Science:

  • Network analysis
  • Graph theory
  • Machine learning

Background:

  • Betweenness-centrality quantifies node importance in networks by analyzing shortest paths.
  • Calculating exact betweenness-centrality is computationally expensive for large graphs.
  • Approximation algorithms are needed to identify top-k important nodes efficiently.

Purpose of the Study:

  • To develop a deep graph convolutional neural network for approximating betweenness-centrality scores.
  • To improve upon existing shallow network approaches for identifying top-k nodes.
  • To achieve faster inference and training with reduced resource consumption.

Main Methods:

  • A deep graph convolutional neural network architecture is proposed.
  • The model outputs a rank score for each node, approximating betweenness-centrality.
  • Advanced optimization and regularization techniques, including Progressive-DropEdge, are employed.

Main Results:

  • The deep graph convolutional network outperforms current shallow network methods.
  • The proposed algorithm demonstrates an order of magnitude faster inference.
  • Training time and resource requirements are significantly reduced compared to existing approaches.

Conclusions:

  • Deep graph convolutional networks offer a powerful and efficient solution for approximating betweenness-centrality.
  • The developed method provides a scalable and resource-efficient alternative for large-scale network analysis.
  • This approach facilitates practical applications in community detection and network dismantling.