https://scholars.lib.ntu.edu.tw/handle/123456789/607258
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.author | Chen H.-W | en_US |
dc.contributor.author | Shuai H.-H | en_US |
dc.contributor.author | Yang D.-N | en_US |
dc.contributor.author | Lee W.-C | en_US |
dc.contributor.author | Shi C | en_US |
dc.contributor.author | Yu P.S | en_US |
dc.contributor.author | MING-SYAN CHEN | en_US |
dc.creator | Chen H.-W;Shuai H.-H;Yang D.-N;Lee W.-C;Shi C;Yu P.S;Chen M.-S. | - |
dc.date.accessioned | 2022-04-25T06:42:59Z | - |
dc.date.available | 2022-04-25T06:42:59Z | - |
dc.date.issued | 2021 | - |
dc.identifier.issn | 10844627 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112868201&doi=10.1109%2fICDE51399.2021.00203&partnerID=40&md5=ad261f282a8a341345c865036f79bb09 | - |
dc.identifier.uri | https://scholars.lib.ntu.edu.tw/handle/123456789/607258 | - |
dc.description.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. | - |
dc.relation.ispartof | Proceedings - International Conference on Data Engineering | - |
dc.subject | Graph neural network | - |
dc.subject | Group quer | - |
dc.subject | HIN | - |
dc.subject | Transformer | - |
dc.subject | Social networking (online) | - |
dc.subject | Activity informations | - |
dc.subject | Heterogeneous information | - |
dc.subject | Learning Based Models | - |
dc.subject | On-line social networks | - |
dc.subject | Research communities | - |
dc.subject | State-of-the-art methods | - |
dc.subject | Structure-aware | - |
dc.subject | Team formation | - |
dc.subject | Information services | - |
dc.title | Structure-aware parameter-free group query via heterogeneous information network transformer | en_US |
dc.type | conference paper | en |
dc.relation.conference | 37th IEEE International Conference on Data Engineering, ICDE 2021 | - |
dc.identifier.doi | 10.1109/ICDE51399.2021.00203 | - |
dc.identifier.scopus | 2-s2.0-85112868201 | - |
dc.relation.pages | 2075-2080 | - |
dc.relation.journalvolume | 2021-April | - |
item.cerifentitytype | Publications | - |
item.fulltext | no fulltext | - |
item.openairecristype | http://purl.org/coar/resource_type/c_5794 | - |
item.openairetype | conference paper | - |
item.grantfulltext | none | - |
crisitem.author.dept | Electrical Engineering | - |
crisitem.author.dept | Computer Science and Information Engineering | - |
crisitem.author.dept | Communication Engineering | - |
crisitem.author.dept | Networking and Multimedia | - |
crisitem.author.orcid | 0000-0002-0711-8197 | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
crisitem.author.parentorg | College of Electrical Engineering and Computer Science | - |
顯示於: | 電機工程學系 |
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