Multi-view network embedding via graph factorization clustering and co-regularized multi-view agreement

Yiwei Sun, Ngot Bui, Tsung Yu Hsieh, Vasant Honavar

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

3 Scopus citations

Abstract

Real-world social networks and digital platforms are comprised of individuals (nodes) that are linked to other individuals or entities through multiple types of relationships (links). Sub-networks of such a network based on each type of link correspond to distinct views of the underlying network. In real-world applications each node is typically linked to only a small subset of other nodes. Hence, practical approaches to problems such as node labeling have to cope with the resulting sparse networks. While low-dimensional network embeddings offer a promising approach to this problem, most of the current network embedding methods focus primarily on single view networks. We introduce a novel multi-view network embedding (MVNE) algorithm for constructing low-dimensional node embeddings from multi-view networks. MVNE adapts and extends an approach to single view node embedding using graph factorization clustering (GFC) to the multi-view setting using an objective function that maximizes the agreement between views based on both the local and global structure of the underlying multi-view graph. Our experiments with several benchmark real-world single view networks show that SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks. Our experiments with several multi-view networks show that MVNE substantially outperforms the single view methods on integrated view and the state-of-the-art multi-view methods. We further show that even when the goal is to predict labels of nodes within a single target view, MVNE outperforms its single-view counterpart suggesting that the MVNE is able to extract the information that is useful for labeling nodes in the target view from the all of the views.

Original languageEnglish (US)
Title of host publicationProceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
EditorsZhenhui Li, Hanghang Tong, Feida Zhu, Jeffrey Yu
PublisherIEEE Computer Society
Pages1006-1013
Number of pages8
ISBN (Electronic)9781538692882
DOIs
StatePublished - Feb 7 2019
Event18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 - Singapore, Singapore
Duration: Nov 17 2018Nov 20 2018

Publication series

NameIEEE International Conference on Data Mining Workshops, ICDMW
Volume2018-November
ISSN (Print)2375-9232
ISSN (Electronic)2375-9259

Conference

Conference18th IEEE International Conference on Data Mining Workshops, ICDMW 2018
CountrySingapore
CitySingapore
Period11/17/1811/20/18

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All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Software

Cite this

Sun, Y., Bui, N., Hsieh, T. Y., & Honavar, V. (2019). Multi-view network embedding via graph factorization clustering and co-regularized multi-view agreement. In Z. Li, H. Tong, F. Zhu, & J. Yu (Eds.), Proceedings - 18th IEEE International Conference on Data Mining Workshops, ICDMW 2018 (pp. 1006-1013). [8637384] (IEEE International Conference on Data Mining Workshops, ICDMW; Vol. 2018-November). IEEE Computer Society. https://doi.org/10.1109/ICDMW.2018.00145