A View-Adversarial Framework for Multi-View Network Embedding

Dongqi Fu, Zhe Xu, Bo Li, Hanghang Tong, Jingrui He

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

Abstract

Network embedding has demonstrated effective empirical performance for various network mining tasks such as node classification, link prediction, clustering, and anomaly detection. However, most of these algorithms focus on the single-view network scenario. From a real-world perspective, one individual node can have different connectivity patterns in different networks. For example, one user can have different relationships on Twitter, Facebook, and LinkedIn due to varying user behaviors on different platforms. In this case, jointly considering the structural information from multiple platforms (i.e., multiple views) can potentially lead to more comprehensive node representations, and eliminate noises and bias from a single view. In this paper, we propose a view-adversarial framework to generate comprehensive and robust multi-view network representations named VANE, which is based on two adversarial games. The first adversarial game enhances the comprehensiveness of the node representation by discriminating the view information which is obtained from the subgraph induced by neighbors of that node. The second adversarial game improves the robustness of the node representation with the challenging of fake node representations from the generative adversarial net. We conduct extensive experiments on downstream tasks with real-world multi-view networks, which shows that our proposed VANE framework significantly outperforms other baseline methods.

Original languageEnglish (US)
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2025-2028
Number of pages4
ISBN (Electronic)9781450368599
DOIs
StatePublished - Oct 19 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: Oct 19 2020Oct 23 2020

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
CountryIreland
CityVirtual, Online
Period10/19/2010/23/20

Keywords

  • adversarial learning
  • multi-view network
  • network embedding

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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