Towards unsupervised deep graph structure learning

Yixin Liu, Yu Zheng, Daokun Zhang, Hongxu Chen, Hao Peng, Shirui Pan

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

In recent years, graph neural networks (GNNs) have emerged as a successful tool in a variety of graph-related applications. However, the performance of GNNs can be deteriorated when noisy connections occur in the original graph structures; besides, the dependence on explicit structures prevents GNNs from being applied to general unstructured scenarios. To address these issues, recently emerged deep graph structure learning (GSL) methods propose to jointly optimize the graph structure along with GNN under the supervision of a node classification task. Nonetheless, these methods focus on a supervised learning scenario, which leads to several problems, i.e., the reliance on labels, the bias of edge distribution, and the limitation on application tasks. In this paper, we propose a more practical GSL paradigm, unsupervised graph structure learning, where the learned graph topology is optimized by data itself without any external guidance (i.e., labels). To solve the unsupervised GSL problem, we propose a novel StrUcture Bootstrapping contrastive LearnIng fraMEwork (SUBLIME for abbreviation) with the aid of self-supervised contrastive learning. Specifically, we generate a learning target from the original data as an "anchor graph", and use a contrastive loss to maximize the agreement between the anchor graph and the learned graph. To provide persistent guidance, we design a novel bootstrapping mechanism that upgrades the anchor graph with learned structures during model learning. We also design a series of graph learners and post-processing schemes to model the structures to learn. Extensive experiments on eight benchmark datasets demonstrate the significant effectiveness of our proposed SUBLIME and high quality of the optimized graphs.

Original languageEnglish
Title of host publicationWWW 2022 - Proceedings of the ACM Web Conference 2022
EditorsElena Simperl, Deepak Agarwal, Aristides Gionis
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1392-1403
Number of pages12
ISBN (Print)9781450390965
DOIs
Publication statusPublished - 2022
EventInternational World Wide Web Conference 2022 - Virtual, Online, France
Duration: 25 Apr 202229 Apr 2022
Conference number: 31st
https://www2022.thewebconf.org/

Conference

ConferenceInternational World Wide Web Conference 2022
Abbreviated titleWWW 2022
Country/TerritoryFrance
CityVirtual, Online
Period25/04/2229/04/22
Internet address

Keywords

  • contrastive learning
  • graph neural networks
  • graph structure learning
  • unsupervised learning

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