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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Vydané v:	The VLDB journal Ročník 26; číslo 6; s. 751 - 776
Hlavní autori:	Li, Rong-Hua, Qin, Lu, Yu, Jeffrey Xu, Mao, Rui
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2017 Springer Nature B.V
Predmet:	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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Abstract	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.
AbstractList	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-rk-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-rk-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-rk-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. 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.
Author	Qin, Lu Yu, Jeffrey Xu Mao, Rui Li, Rong-Hua
Author_xml	– sequence: 1 givenname: Rong-Hua surname: Li fullname: Li, Rong-Hua organization: College of Computer Science and Software Engineering, Shenzhen University – sequence: 2 givenname: Lu surname: Qin fullname: Qin, Lu organization: Centre for QCIS, FEIT, University of Technology – sequence: 3 givenname: Jeffrey Xu surname: Yu fullname: Yu, Jeffrey Xu email: yu@se.cuhk.edu.cn organization: The Chinese University of Hong Kong – sequence: 4 givenname: Rui surname: Mao fullname: Mao, Rui email: mao@szu.edu.cn organization: College of Computer Science and Software Engineering, Shenzhen University
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Cites_doi	10.1137/0214017 10.1145/2463676.2463704 10.1109/ICDE.2011.5767911 10.2307/3088904 10.1016/0378-8733(83)90028-X 10.1109/ICDMW.2006.76 10.1145/2339530.2339724 10.1145/2043652.2043654 10.1109/ICDE.2012.35 10.1145/2588555.2612179 10.1109/TKDE.2013.158 10.1145/2463676.2465323 10.1145/2505515.2505751 10.1073/pnas.1116502109 10.1145/2247596.2247652 10.1109/ICDE.2016.7498235 10.1145/2588555.2610495 10.1016/j.tcs.2011.12.006 10.1016/j.physrep.2009.11.002 10.1145/2463676.2463722 10.1145/2723372.2723740 10.1016/j.comnet.2012.09.011 10.1007/s11634-010-0079-y 10.1145/2463676.2463703 10.1145/1835804.1835923
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Netw.19835326928710.1016/0378-8733(83)90028-X721295 – reference: ChengJKeYFuAWCYuJXZhuLFinding maximal cliques in massive networksACM Trans. Database Syst.20113642110.1145/2043652.2043654 – reference: Saito, K., Yamada, T.: Extracting communities from complex networks by the k-dense method. In: ICDM Workshops (2006) – reference: LiRYuJXMaoREfficient core maintenance in large dynamic graphsIEEE Trans. Knowl. Data Eng.201426102453246510.1109/TKDE.2013.158 – reference: WangNZhangJTanKLTungAKHOn triangulation-based dense neighborhood graphs discoveryPVLDB2010425868 – reference: ZhaoFTungAKHLarge scale cohesive subgraphs discovery for social network visual analysisPVLDB2012628596 – reference: BatageljVZaversnikMFast algorithms for determining (generalized) core groups in social networksAdv. Data Anal. 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Snippet	Community search is a problem of finding densely connected subgraphs that satisfy the query conditions in a network, which has attracted much attention in...
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Title	Finding influential communities in massive networks
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