Pseudoarboricity-Based Skyline Important Community Search in Large Networks

Important communities are densely connected subgraphs containing vertices with high importance values, which have received wide attention recently. However, existing methods, predominantly based on the <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-for...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE transactions on knowledge and data engineering s. 1 - 16
Hlavní autori: Jiang, Jiaqi, Li, Rong-Hua, Lin, Longlong, Zhang, Yalong, Zeng, Yue, Ye, Xiaowei, Wang, Guoren
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: IEEE 2025
Predmet:
Computational modeling
Divide-and-Conquer
important community
Indexes
Measurement
Message systems
parallel algorithm
Parallel algorithms
pseudoarboricity
Resilience
Scalability
skyline computation
Social networking (online)
Time complexity
Trees (botanical)
ISSN:1041-4347, 1558-2191
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:Important communities are densely connected subgraphs containing vertices with high importance values, which have received wide attention recently. However, existing methods, predominantly based on the <inline-formula><tex-math notation="LaTeX">k</tex-math></inline-formula>-core model, suffer from limitations such as rigid degree constraints and suboptimal density, often failing to capture highly important vertices. To address these limitations, we propose a new community model based on pseudoarboricity that guarantees near-optimal density while preserving important vertices. Further, we introduce a novel problem of Psudoarboricity-based Skyline Important Community (PSIC), which uniquely treats density and importance as independent attributes. To efficiently address PSIC, we first devise a basic algorithm climbStairs, which iteratively refines communities by peeling vertices with low importance. To boost efficiency, we develop an advanced algorithm DivAndCon, which employs a recursive divide-and-conquer strategy combined with weight-based and pseudoarboricity-based pruning techniques, significantly reducing the search space. For massive graphs with billions of edges, inspired by a recursive division tree, we develop several parallel algorithms utilizing thread-pool and free-synchronization mechanism. Finally, we conduct extensive experiments on 10 real-world networks, and the results demonstrate the superiority of our solutions in terms of effectiveness, efficiency, and scalability.
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2025.3631112