Graph-Based Node Finding in Big Complex Contextual Social Graphs
Joint Authors
Lu, Junwen
Liu, Guanfeng
Wu, Keshou
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-26
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
Graph pattern matching is to find the subgraphs matching the given pattern graphs.
In complex contextual social networks, considering the constraints of social contexts like the social relationships, the social trust, and the social positions, users are interested in the top-K matches of a specific node (denoted as the designated node) based on a pattern graph, rather than the entire set of graph matching.
This inspires the conText-Aware Graph pattern-based top-K designated node matching (TAG-K) problem, which is NP-complete.
Targeting this challenging problem, we propose a recurrent neural network- (RNN-) based Monte Carlo Tree Search algorithm (RN-MCTS), which automatically balances exploring new possible matches and extending existing matches.
The RNN encodes the subgraph and maps it to a policy which is used to guide the MCTS.
The experimental results demonstrate that our proposed algorithm outperforms the state-of-the-art methods in terms of both efficiency and effectiveness.
American Psychological Association (APA)
Wu, Keshou& Liu, Guanfeng& Lu, Junwen. 2020. Graph-Based Node Finding in Big Complex Contextual Social Graphs. Complexity،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1143968
Modern Language Association (MLA)
Wu, Keshou…[et al.]. Graph-Based Node Finding in Big Complex Contextual Social Graphs. Complexity No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1143968
American Medical Association (AMA)
Wu, Keshou& Liu, Guanfeng& Lu, Junwen. Graph-Based Node Finding in Big Complex Contextual Social Graphs. Complexity. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1143968
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1143968