Finding influential communities in massive networks

Community search is a problem of finding densely connected subgraphs that satisfy the query conditions in a network, which has attracted much attention in recent years. However, all the previous studies on community search do not consider the influence of a community. In this paper, we introduce a n...

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Veröffentlicht in:The VLDB journal Jg. 26; H. 6; S. 751 - 776
Hauptverfasser: Li, Rong-Hua, Qin, Lu, Yu, Jeffrey Xu, Mao, Rui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017
Springer Nature B.V
Schlagworte:
Algorithms
Communities
Computer Science
Data structures
Database Management
Regular Paper
Search algorithms
Supports
Core decomposition
Tree-shape data structure
Influential community
Dynamic graph
I/O-efficient algorithm
ISSN:1066-8888, 0949-877X
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Zusammenfassung:Community search is a problem of finding densely connected subgraphs that satisfy the query conditions in a network, which has attracted much attention in recent years. However, all the previous studies on community search do not consider the influence of a community. In this paper, we introduce a novel community model called k -influential community based on the concept of k -core to capture the influence of a community. Based on this community model, we propose a linear time online search algorithm to find the top- r k -influential communities in a network. To further speed up the influential community search algorithm, we devise a linear space data structure which supports efficient search of the top- r k -influential communities in optimal time. We also propose an efficient algorithm to maintain the data structure when the network is frequently updated. Additionally, we propose a novel I/O-efficient algorithm to find the top- r k -influential communities in a disk-resident graph under the assumption of U = O ( n ) , where U and n denote the size of the main memory and the number of nodes, respectively. Finally, we conduct extensive experiments on six real-world massive networks, and the results demonstrate the efficiency and effectiveness of the proposed methods.
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ISSN:1066-8888
0949-877X
DOI:10.1007/s00778-017-0467-4