Flexible and Robust Co-Regularized Multi-Domain Graph Clustering
Abstract
Multi-view graph clustering aims to enhance clustering performance by integrating heterogeneous information collected in different do- mains. Each domain provides a different view of the data instances. Leveraging cross-domain information has been demonstrated an effective way to achieve better clustering results. Despite the previous success, existing multi-view graph clustering methods usually assume that different views are available for the same set of in- stances. Thus instances in different domains can be treated as having strict one-To-one relationship. In many real-life applications, however, data instances in one domain may correspond to multiple instances in another domain. Moreover, relationships between in- stances in different domains may be associated with weights based on prior (partial) knowledge. In this paper, we propose a flexible and robust framework, CGC (Co-regularized Graph Clustering), based on non-negative matrix factorization (NMF), to tackle these challenges. CGC has several advantages over the existing methods. First, it supports many-to-many cross-domain instance relation- ship. Second, it incorporates weight on cross-domain relationship. Third, it allows partial cross-domain mapping so that graphs in different domains may have different sizes. Finally, it provides users with the extent to which the cross-domain instance relationship violates the in-domain clustering structure, and thus enables users to re-evaluate the consistency of the relationship. Extensive experimental results on UCI benchmark data sets, newsgroup data sets and biological interaction networks demonstrate the effectiveness of our approach.
Recommended Citation
W. Cheng et al., "Flexible and Robust Co-Regularized Multi-Domain Graph Clustering," Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2013, Chicago, IL), vol. Part F128815, pp. 320 - 328, Association for Computing Machinery (ACM), Aug 2013.
The definitive version is available at https://doi.org/10.1145/2487575.2487582
Meeting Name
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2013: Aug. 11-14, Chicago, IL)
Department(s)
Computer Science
Keywords and Phrases
Co-Regularization; Graph Clustering; Nonnegative Matrix Factorization
International Standard Book Number (ISBN)
978-145032174-7
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2013 Association for Computing Machinery (ACM), All rights reserved.
Publication Date
01 Aug 2013
