Graph Clustering via Cohesiveness-aware Vector Partitioning
Pages 33 - 40
Abstract
Graph clustering is one of the key techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, modularity-based graph clustering algorithm is successfully used in many applications. However, it is difficult for the modularity-based methods to find fine-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, CAV-Partitioning, that shows better clustering results than the traditional algorithm. In our proposed method, we introduce cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Extensive experiments on public datasets demonstrate the performance superiority of CAV-Partitioning over the state-of-the-art approaches.
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Index Terms
- Graph Clustering via Cohesiveness-aware Vector Partitioning
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Published In
November 2018
419 pages
ISBN:9781450364799
DOI:10.1145/3282373
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- Johannes Kepler University, Linz, Austria
- @WAS: International Organization of Information Integration and Web-based Applications and Services
- Johannes Kepler University
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 19 November 2018
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iiWAS2018
iiWAS2018: 20th International Conference on Information Integration and Web-based Applications & Services
November 19 - 21, 2018
Yogyakarta, Indonesia
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