Name
Title | ||
|---|---|---|
Structure-Aware Parameter-Free Group Query Via Heterogeneous Information Network Transformer |
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 Fl-score on three public datasets. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICDE51399.2021.00203 | 2021 IEEE 37TH INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2021) |
DocType | ISSN | Citations |
Conference | 1084-4627 | 0 |
PageRank | References | Authors |
0.34 | 0 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hsi-Wen Chen | 1 | 0 | 1.01 |
| Hong-Han Shuai | 2 | 100 | 24.80 |
| De-Nian Yang | 3 | 586 | 66.66 |
| Wang-Chien Lee | 4 | 5765 | 346.32 |
| Chuan Shi | 5 | 1137 | 80.79 |
| Philip S. Yu | 6 | 30670 | 3474.16 |
| Ming-Syan Chen | 7 | 59 | 5.65 |