TrustLOG: The First Workshop on Trustworthy Learning on Graphs

Jian Kang, Shuaicheng Zhang, Bo Li, Jingrui He, Jian Pei, Dawei Zhou

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

Abstract

Learning on graphs (LOG) plays a pivotal role in various high-impact application domains. The past decades have developed tremendous theories, algorithms, and open-source systems in answering what/who questions on graphs. However, recent studies reveal that the state-of-the-art techniques for learning on graphs (LOG) are often not trustworthy in practice with respect to several social aspects (e.g., fairness, transparency, security). A natural research question to ask is: how can we make learning algorithms on graphs trustworthy? To answer this question, we propose a paradigm shift, from answering what and who LOG questions to understanding how and why LOG questions. The TrustLOG workshop provides a venue for presenting, discussing, and promoting frontier research on trustworthy learning on graphs. Moreover, TrustLOG will serve as an impulse for the LOG community to identify novel research problems and shed new light on future directions.

Original languageEnglish (US)
Title of host publicationCIKM 2022 - Proceedings of the 31st ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages5169-5170
Number of pages2
ISBN (Electronic)9781450392365
DOIs
StatePublished - Oct 17 2022
Event31st ACM International Conference on Information and Knowledge Management, CIKM 2022 - Atlanta, United States
Duration: Oct 17 2022Oct 21 2022

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference31st ACM International Conference on Information and Knowledge Management, CIKM 2022
Country/TerritoryUnited States
CityAtlanta
Period10/17/2210/21/22

Keywords

  • graph learning
  • graph mining
  • trustworthiness

ASJC Scopus subject areas

  • General Business, Management and Accounting
  • General Decision Sciences

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