Abstract
In this paper, we propose and study a novel cohesive subgraph model, named (\(k\),\(s\))-core, which requires each user to have at least k familiars or friends (not just acquaintances) in the subgraph. The model considers both user engagement and tie strength to discover strong communities. We compare the (\(k\),\(s\))-core model with \(k\)-core and \(k\)-truss theoretically and experimentally. We propose efficient algorithms to compute the (\(k\),\(s\))-core and decompose the graph by a particular sub-model \(k\)-fami. Extensive experiments show (1) our (\(k\),\(s\))-core and \(k\)-fami are effective cohesive subgraph models and (2) the (\(k\),\(s\))-core computation and \(k\)-fami decomposition are efficient on various real-life social networks.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akbas, E., Zhao, P.: Truss-based community search: a truss-equivalence based indexing approach. PVLDB 10(11), 1298–1309 (2017)
Batagelj, V., Zaversnik, M.: An O(m) algorithm for cores decomposition of networks. CoRR, cs.DS/0310049 (2003)
Batagelj, V., Zaversnik, M.: Fast algorithms for determining (generalized) core groups in social networks. Adv. Data Anal. Classif. 5(2), 129–145 (2011)
Bhawalkar, K., Kleinberg, J., Lewi, K., Roughgarden, T., Sharma, A.: Preventing unraveling in social networks: the anchored k-core problem. SIAM J. Discrete Math. 29(3), 1452–1475 (2015)
Bron, C., Kerbosch, J.: Finding all cliques of an undirected graph (algorithm 457). Commun. ACM 16(9), 575–576 (1973)
Cohen, J.: Trusses: cohesive subgraphs for social network analysis. National Security Agency Technical Report, p. 16 (2008)
Granovetter, M.S.: The strength of weak ties. Am. J. Sociol. 78(6), 1360–1380 (1973)
Huang, X., Cheng, H., Qin, L., Tian, W., Yu, J.X.: Querying k-truss community in large and dynamic graphs. In: SIGMOD, pp. 1311–1322 (2014)
Huang, X., Lakshmanan, L.V.S.: Attribute-driven community search. PVLDB 10(9), 949–960 (2017)
Khaouid, W., Barsky, M., Venkatesh, S., Thomo, A.: K-core decomposition of large networks on a single PC. PVLDB 9(1), 13–23 (2015)
Lee, P., Lakshmanan, L.V.S., Milios, E.E.: CAST: a context-aware story-teller for streaming social content. In: CIKM, pp. 789–798 (2014)
Luce, R.D., Perry, A.D.: A method of matrix analysis of group structure. Psychometrika 14(2), 95–116 (1949)
Rotabi, R., Kamath, K., Kleinberg, J.M., Sharma, A.: Detecting strong ties using network motifs. In: WWW, pp. 983–992 (2017)
Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)
Seidman, S.B., Foster, B.L.: A graph-theoretic generalization of the clique concept. J. Math. Sociol. 6(1), 139–154 (1978)
Shao, Y., Chen, L., Cui, B.: Efficient cohesive subgraphs detection in parallel. In: SIGMOD, pp. 613–624 (2014)
Ugander, J., Backstrom, L., Marlow, C., Kleinberg, J.: Structural diversity in social contagion. PNAS 109(16), 5962–5966 (2012)
Wang, J., Cheng, J.: Truss decomposition in massive networks. PVLDB 5(9), 812–823 (2012)
Zhang, F., Zhang, W., Zhang, Y., Qin, L., Lin, X.: OLAK: an efficient algorithm to prevent unraveling in social networks. PVLDB 10(6), 649–660 (2017)
Zhang, F., Zhang, Y., Qin, L., Zhang, W., Lin, X.: Finding critical users for social network engagement: the collapsed k-core problem. In: AAAI, pp. 245–251 (2017)
Zhang, F., Zhang, Y., Qin, L., Zhang, W., Lin, X.: When engagement meets similarity: efficient (k, r)-core computation on social networks. PVLDB 10(10), 998–1009 (2017)
Zhang, Y., Yu, J.X., Zhang, Y., Qin, L.: A fast order-based approach for core maintenance. In: ICDE, pp. 337–348 (2017)
Zhao, F., Tung, A.K.H.: Large scale cohesive subgraphs discovery for social network visual analysis. PVLDB 6(2), 85–96 (2012)
Acknowledgments
Fan Zhang and Long Yuan are supported by Huawei YBN2017100007. Ying Zhang is supported by ARC FT170100128 and DP180103096. Lu Qin is supported by ARC DP160101513. Xuemin Lin is supported by NSFC 61672235, ARC DP170101628, DP180103096 and Huawei YBN2017100007.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Zhang, F., Yuan, L., Zhang, Y., Qin, L., Lin, X., Zhou, A. (2018). Discovering Strong Communities with User Engagement and Tie Strength. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds) Database Systems for Advanced Applications. DASFAA 2018. Lecture Notes in Computer Science(), vol 10827. Springer, Cham. https://doi.org/10.1007/978-3-319-91452-7_28
Download citation
DOI: https://doi.org/10.1007/978-3-319-91452-7_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91451-0
Online ISBN: 978-3-319-91452-7
eBook Packages: Computer ScienceComputer Science (R0)