research-article

Overlapping Graph Clustering in Attributed Networks via Generalized Cluster Potential Game

Authors:

Jie CaoAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 18, Issue 1

Article No.: 27, Pages 1 - 26

https://doi.org/10.1145/3597436

Published: 16 October 2023 Publication History

Abstract

Overlapping graph clustering is essential to understand the nature and behavior of real complex systems including human interactions, technical systems and transportation network. However, in addition of topological structure, many real-world networked systems contain spare factors, i.e., attributes of networks. Despite the considerable efforts that have been made in graph clustering, they only concentrate on the topological structure, which lack a profound understanding of cluster configuration on attributed graphs. To address this great challenge, in this article, we propose a new overlapping graph clustering algorithm by integrating the topological and attributive information into a cluster potential game (CPG). Firstly, a generalized definition of the utility function is provided, which measures the payoff of each node based on different node-to-cluster distance functions. It is worth mentioning that the model we proposed is able to associate with the classic ordinal potential game well. Then, we define the measures of both tightness and the homogeneity in each cluster, and introduce a novel two-way selection mechanism. The goal is to extend the flexibility of the cluster potential game, so that one can achieve a win-win situation between nodes and clusters. Finally, a distributed and heterogeneous multiagent system (DHMAS) is carefully designed based on a fast self-learning algorithm (SLA) for attributed overlapping graph clustering. Two series of experiments are implemented in multi-types datasets and the results verify the effectiveness and the scalability after the comparison with the most advanced approaches of literature.

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Index Terms

Overlapping Graph Clustering in Attributed Networks via Generalized Cluster Potential Game
1. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 18, Issue 1

January 2024

854 pages

EISSN:1556-472X

DOI:10.1145/3613504

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Publication History

Published: 16 October 2023

Online AM: 18 May 2023

Accepted: 08 May 2023

Revised: 23 December 2022

Received: 12 November 2021

Published in TKDD Volume 18, Issue 1

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Fundamental Research Funds for the Central Universities of China (Nankai University)
National Natural Science Foundation of China

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