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Renyi entropy driven hierarchical graph clustering.

Frédérique Oggier1, Anwitaman Datta2

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Summary
This summary is machine-generated.

This study introduces a novel graph clustering method using Renyi entropy. It demonstrates consistent results across two distinct approaches for analyzing complex data structures.

Keywords:
Graph clusteringRenyi entropy

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Area of Science:

  • Information Theory
  • Graph Theory
  • Data Mining

Background:

  • Graph clustering is crucial for analyzing complex networks.
  • Existing methods may not fully capture the nuances of graph structures.
  • Information-theoretic approaches offer a robust framework for data analysis.

Purpose of the Study:

  • To develop and evaluate a graph clustering method based on Renyi entropy.
  • To compare two distinct strategies for applying this method to graph data.
  • To assess the consistency and effectiveness of the proposed approach.

Main Methods:

  • Utilized Renyi entropy for clustering points in Euclidean space.
  • Employed graph embedding into Euclidean space to minimize distortion before clustering.
  • Applied shortest path and Jaccard distances for direct graph clustering.
  • Implemented a hierarchical clustering approach with entropy-derived evaluation functions.

Main Results:

  • Both graph embedding and direct clustering methods yielded consistent results.
  • The hierarchical approach effectively utilized Renyi entropy for agglomeration.
  • Numerical examples validated the efficacy of the proposed clustering techniques.

Conclusions:

  • The Renyi entropy-based graph clustering method is effective and consistent.
  • The two explored viewpoints offer viable strategies for graph analysis.
  • This approach provides a powerful tool for uncovering structures in complex networks.