Deep multi-graph clustering via attentive cross-graph association

Dongsheng Luo, Jingchao Ni, Suhang Wang, Yuchen Bian, Xiong Yu, Xiang Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

Multi-graph clustering aims to improve clustering accuracy by leveraging information from different domains, which has been shown to be extremely effective for achieving better clustering results than single graph based clustering algorithms. Despite the previous success, existing multi-graph clustering methods mostly use shallow models, which are incapable to capture the highly non-linear structures and the complex cluster associations in multi-graph, thus result in sub-optimal results. Inspired by the powerful representation learning capability of neural networks, in this paper, we propose an end-to-end deep learning model to simultaneously infer cluster assignments and cluster associations in multi-graph. Specifically, we use autoencoding networks to learn node embeddings. Meanwhile, we propose a minimum-entropy based clustering strategy to cluster nodes in the embedding space for each graph. We introduce two regularizers to leverage both within-graph and cross-graph dependencies. An attentive mechanism is further developed to learn cross-graph cluster associations. Through extensive experiments on a variety of datasets, we observe that our method outperforms state-of-the-art baselines by a large margin.

Original languageEnglish (US)
Title of host publicationWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages393-401
Number of pages9
ISBN (Electronic)9781450368223
DOIs
StatePublished - Jan 20 2020
Event13th ACM International Conference on Web Search and Data Mining, WSDM 2020 - Houston, United States
Duration: Feb 3 2020Feb 7 2020

Publication series

NameWSDM 2020 - Proceedings of the 13th International Conference on Web Search and Data Mining

Conference

Conference13th ACM International Conference on Web Search and Data Mining, WSDM 2020
CountryUnited States
CityHouston
Period2/3/202/7/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software
  • Computer Science Applications

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