Unbiased Graph Embedding with Biased Graph Observations

Nan Wang, Lu Lin, Jundong Li, Hongning Wang

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

8 Scopus citations

Abstract

Graph embedding techniques are pivotal in real-world machine learning tasks that operate on graph-structured data, such as social recommendation and protein structure modeling. Embeddings are mostly performed on the node level for learning representations of each node. Since the formation of a graph is inevitably affected by certain sensitive node attributes, the node embeddings can inherit such sensitive information and introduce undesirable biases in downstream tasks. Most existing works impose ad-hoc constraints on the node embeddings to restrict their distributions for unbiasedness/fairness, which however compromise the utility of the resulting embeddings. In this paper, we propose a principled new way for unbiased graph embedding by learning node embeddings from an underlying bias-free graph, which is not influenced by sensitive node attributes. Motivated by this new perspective, we propose two complementary methods for uncovering such an underlying graph, with the goal of introducing minimum impact on the utility of the embeddings. Both our theoretical justification and extensive experimental comparisons against state-of-the-art solutions demonstrate the effectiveness of our proposed methods.

Original languageEnglish (US)
Title of host publicationWWW 2022 - Proceedings of the ACM Web Conference 2022
PublisherAssociation for Computing Machinery, Inc
Pages1423-1433
Number of pages11
ISBN (Electronic)9781450390965
DOIs
StatePublished - Apr 25 2022
Event31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, France
Duration: Apr 25 2022Apr 29 2022

Publication series

NameWWW 2022 - Proceedings of the ACM Web Conference 2022

Conference

Conference31st ACM World Wide Web Conference, WWW 2022
Country/TerritoryFrance
CityVirtual, Online
Period4/25/224/29/22

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Software

Fingerprint

Dive into the research topics of 'Unbiased Graph Embedding with Biased Graph Observations'. Together they form a unique fingerprint.

Cite this