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Open Access

Game Theoretical Approach for Non-Overlapping Community Detection

Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
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Abstract

Graph clustering, i.e., partitioning nodes or data points into non-overlapping clusters, can be beneficial in a large varieties of computer vision and machine learning applications. However, main graph clustering schemes, such as spectral clustering, cannot be applied to a large network due to prohibitive computational complexity required. While there exist methods applicable to large networks, these methods do not offer convincing comparisons against known ground truth. For the first time, this work conducts clustering algorithm performance evaluations on large networks (consisting of one million nodes) with ground truth information. Ideas and concepts from game theory are applied towards graph clustering to formulate a new proposed algorithm, Game Theoretical Approach for Clustering (GTAC). This theoretical framework is shown to be a generalization of both the Label Propagation and Louvain methods, offering an additional means of derivation and analysis. GTAC introduces a tuning parameter which allows variable algorithm performance in accordance with application needs. Experimentation shows that these GTAC algorithms offer scalability and tunability towards big data applications.

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Tsinghua Science and Technology
Pages 706-723
Cite this article:
Sun B, Al-Bayaty R, Huang Q, et al. Game Theoretical Approach for Non-Overlapping Community Detection. Tsinghua Science and Technology, 2021, 26(5): 706-723. https://doi.org/10.26599/TST.2020.9010017

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Received: 10 December 2019
Revised: 24 May 2020
Accepted: 25 May 2020
Published: 20 April 2021
© The author(s) 2021

© The author(s) 2021. The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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