Structure-aware parameter-free group query via heterogeneous information network transformer
Journal
Proceedings - International Conference on Data Engineering
Journal Volume
2021-April
Pages
2075-2080
Date Issued
2021
Author(s)
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. ? 2021 IEEE.
Event(s)
37th IEEE International Conference on Data Engineering, ICDE 2021
Subjects
Graph neural network
Group quer
HIN
Transformer
Social networking (online)
Activity informations
Heterogeneous information
Learning Based Models
On-line social networks
Research communities
State-of-the-art methods
Structure-aware
Team formation
Information services
SDGs
Type
conference paper