Knowledge-preserving incremental social event detection via heterogeneous GNNs

Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, Jianxin Li, Philip S. Yu

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

1 Citation (Scopus)

Abstract

Social events provide valuable insights into group social behaviors and public concerns and therefore have many applications in fields such as product recommendation and crisis management. The complexity and streaming nature of social messages make it appealing to address social event detection in an incremental learning setting, where acquiring, preserving, and extending knowledge are major concerns. Most existing methods, including those based on incremental clustering and community detection, learn limited amounts of knowledge as they ignore the rich semantics and structural information contained in social data. Moreover, they cannot memorize previously acquired knowledge. In this paper, we propose a novel Knowledge-Preserving Incremental Heterogeneous Graph Neural Network (KPGNN) for incremental social event detection. To acquire more knowledge, KPGNN models complex social messages into unified social graphs to facilitate data utilization and explores the expressive power of GNNs for knowledge extraction. To continuously adapt to the incoming data, KPGNN adopts contrastive loss terms that cope with a changing number of event classes. It also leverages the inductive learning ability of GNNs to efficiently detect events and extends its knowledge from previously unseen data. To deal with large social streams, KPGNN adopts a mini-batch subgraph sampling strategy for scalable training, and periodically removes obsolete data to maintain a dynamic embedding space. KPGNN requires no feature engineering and has few hyperparameters to tune. Extensive experiment results demonstrate the superiority of KPGNN over various baselines.

Original languageEnglish
Title of host publicationThe Web Conference 2021
Subtitle of host publicationProceedings of the World Wide Web Conference, WWW 2021
Place of PublicationNew York, NY
PublisherAssociation for Computing Machinery, Inc
Pages3383-3395
Number of pages13
ISBN (Electronic)9781450383127
DOIs
Publication statusPublished - 2021
Event2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia
Duration: 19 Apr 202123 Apr 2021

Publication series

NameThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021

Conference

Conference2021 World Wide Web Conference, WWW 2021
CountrySlovenia
CityLjubljana
Period19/04/2123/04/21

Bibliographical note

Copyright the Publisher 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Contrastive Learning
  • Graph Neural Networks
  • Incremental Learning
  • Social Event Detection

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