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A D-Truss-Equivalence Based Index for Community Search Over Large Directed Graphs

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成果类型:
期刊论文
作者:
Ai, Wei;Xie, Canhao;Meng, Tao;Du, Jayi;Li, Keqin
通讯作者:
Meng, T
作者机构:
[Ai, Wei; Meng, Tao; Meng, T; Xie, Canhao; Du, Jayi] Cent South Univ Forestry & Technol, Coll Comp & Math, Changsha 410004, Hunan, Peoples R China.
[Li, Keqin] SUNY Coll New Paltz, Dept Comp Sci, New York, NY 12561 USA.
通讯机构:
[Meng, T ] C
Cent South Univ Forestry & Technol, Coll Comp & Math, Changsha 410004, Hunan, Peoples R China.
语种:
英文
关键词:
Indexes;Directed graphs;Deep learning;Neural networks;Maintenance;Indexing;Heuristic algorithms;Community search;directed graphs;D-truss;D-truss-equivalence
期刊:
IEEE Transactions on Knowledge and Data Engineering
ISSN:
1041-4347
年:
2024
卷:
36
期:
11
页码:
5482-5494
基金类别:
National Natural Science Foundation of China [69189338]; Excellent Young Scholars of Hunan Province of China [22B0275]; Changsha Natural Science Foundation [kq2202294]
机构署名:
本校为其他机构
院系归属:
计算机与信息工程学院
摘要:
Community Search (CS) aims to enable online and personalized discovery of communities. Recently, attention to the CS problem in directed graphs (di-graph) needs to be improved despite the extensive study conducted on undirected graphs. Nevertheless, the existing studies are plagued by several shortcomings, e.g., Achieving high-performance CS while ensuring the retrieved community is cohesive is challenging. This paper uses the D-truss model to address the limitations of investigating the CS problem in large di-graphs. We aim to implement millisecond-level D-truss CS in di-graphs by building a ...

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