Structure-aware parameter-free group query via heterogeneous information network transformer

Hsi Wen Chen, Hong Han Shuai, De Nian Yang, Wang Chien Lee, Chuan Shi, Philip S. Yu, Ming Syan Chen

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

Abstract

Owing to a wide range of important applications, such as team formation, dense subgraph discovery, and activity attendee suggestions on online social networks, Group Query attracts a lot of attention from the research community. However, most existing works are constrained by a unified social tightness k (e.g., for k-core, or k-plex), without considering the diverse preferences of social cohesiveness in individuals. In this paper, we introduce a new group query, namely Parameter-free Group Query (PGQ), and propose a learning-based model, called PGQN, to find a group that accommodates personalized requirements on social contexts and activity topics. First, PGQN extracts node features by a GNN-based method on Heterogeneous Activity Information Network (HAIN). Then, we transform the PGQ into a graph-to-set (Graph2Set) problem to learn the diverse user preference on topics and members, and find new attendees to the group. Experimental results manifest that our proposed model outperforms nine state-of-the-art methods by at least 51% in terms of F1-score on three public datasets.

Original languageEnglish (US)
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PublisherIEEE Computer Society
Pages2075-2080
Number of pages6
ISBN (Electronic)9781728191843
DOIs
StatePublished - Apr 2021
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: Apr 19 2021Apr 22 2021

Publication series

NameProceedings - International Conference on Data Engineering
Volume2021-April
ISSN (Print)1084-4627

Conference

Conference37th IEEE International Conference on Data Engineering, ICDE 2021
Country/TerritoryGreece
CityVirtual, Chania
Period4/19/214/22/21

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Information Systems

Cite this