Graph Coarsening using Game Theoretic Approach

TMLR Paper6222 Authors

Abstract:

Graph coarsening is a method for reducing the size of an original graph while preserving its structural and feature-related properties. In graph machine learning, it is often employed as a preprocessing step to improve efficiency and scalability when handling large graph
datasets. In this study, we address the challenge of coarsening an original graph into a coarsened graph that retains these characteristics. We propose a Cooperative-Based Graph Coarsening (CGC) algorithm, which leverages cooperative game theory as a framework
for combinatorial optimization, aiming to minimize the total Dirichlet energy of the graph through localized optimizations. We prove that the proposed coarsening game is a potential game that guarantees convergence to a stable coarsened graph. Tests on real-world datasets
demonstrate that CGC algorithm surpasses prior state-of-the-art techniques in terms of coarsened graph accuracy and achieves reduced time complexity. These results highlight the potential of game-theoretic approaches in the advancement of graph coarsening techniques.

Submission Type: Regular submission (no more than 12 pages of main content)

Assigned Action Editor: ~Moshe_Eliasof1

Submission Number: 6222