Abstract
Community search (CS) aims to find a cohesive community that satisfies query conditions in a given information network. Recent studies have introduced the CS problem into heterogeneous information networks (HINs) that are composed of multi-typed vertices and edges. However, existing works of community search in HINs ignore the influence of vertices and community. To solve this problem, we propose the concept of heterogeneous influence and a new model called heterogeneous k-influential community (\(k{\mathcal{P}}\)-HICs) which is designed by combining the concept of heterogeneous influence, meta-path, and k-core. Based on the model, we then develop three algorithms to find top-r \(k{\mathcal{P}}\)-HICs in the heterogeneous community containing the query vertex. The Basic-Peel and Advanced-Peel algorithms find top-r \(k{\mathcal{P}}\)-HICs by repeatedly peeling the low influential vertices. Considering the fact that top-r \(k{\mathcal{P}}\)-HICs are composed of vertices with high influence, the Reversed-Peel algorithm finds top-r \(k{\mathcal{P}}\)-HICs in a high influence vertices composed set and thus is more efficient. Extensive experiments have been performed on three real large HINs, and the results show that the proposed methods are effective for searching top-r \(k{\mathcal{P}}\)-HICs.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3–5), 75–174 (2010)
Javed, M.A., Younis, M.S., Latif, S., Qadir, J., Baig, A.: Community detection in networks: a multidisciplinary review. J. Netw. Comput. Appl. 108, 87–111 (2018)
Sozio, M., Gionis, A.: The community-search problem and how to plan a successful cocktail party. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 939–948 (2010)
Cui, W., Xiao, Y., Wang, H., Wang, W.: Local search of communities in large graphs. In: Proceedings of the 2014 ACM SIGMOD International Conference on Management of Data, pp. 991–1002 (2014)
Shi, C., Li, Y., Zhang, J., Sun, Y., Philip, S.Y.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2016)
Fang, Y., Yang, Y., Zhang, W., Lin, X., Cao, X.: Effective and efficient community search over large heterogeneous information networks. Proc. VLDB Endow. 13(6), 854–867 (2020)
Yang, Y., Fang, Y., Lin, X., Zhang, W.: Effective and efficient truss computation over large heterogeneous information networks. In: 2020 IEEE 36th International Conference on Data Engineering (ICDE), pp. 901–912. IEEE (2020)
Jiang, Y., Fang, Y., Ma, C., Cao, X., Li, C.: Effective community search over large star-schema heterogeneous information networks. Proc. VLDB Endow. 15(11), 2307–2320 (2022)
Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)
Batagelj, V., Zaversnik, M.: An O (m) algorithm for cores decomposition of networks, arXiv preprint cs/0310049 (2003)
Akbas, E., Zhao, P.: Truss-based community search: a truss-equivalence based indexing approach. Proc. VLDB Endow. 10(11), 1298–1309 (2017)
Yuan, L., Qin, L., Zhang, W., Chang, L., Yang, J.: Index-based densest clique percolation community search in networks. IEEE Trans. Knowl. Data Eng. 30(5), 922–935 (2017)
Hu, J., Wu, X., Cheng, R., Luo, S., Fang, Y.: On minimal steiner maximum-connected subgraph queries. IEEE Trans. Knowl. Data Eng. 29(11), 2455–2469 (2017)
Li, R.-H., Qin, L., Yu, J.X., Mao, R.: Finding influential communities in massive networks. VLDB J. 26(6), 751–776 (2017). https://doi.org/10.1007/s00778-017-0467-4
Bi, F., Chang, L., Lin, X., Zhang, W.: An optimal and progressive approach to online search of top-k influential communities, arXiv preprint arXiv:1711.05857 (2017)
Li, J., Wang, X., Deng, K., Yang, X., Sellis, T., Yu, J.X.: Most influential community search over large social networks. In: 2017 IEEE 33rd International Conference on Data Engineering (ICDE), pp. 871–882. IEEE (2017)
Seo, J.H., Kim, M.H.: Finding influential communities in networks with multiple influence types. Inf. Sci. 548, 254–274 (2021)
Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proc. VLDB Endow. 4(11), 992–1003 (2011)
Qiao, L., Zhang, Z., Yuan, Y., Chen, C., Wang, G.: Keyword-centric community search over large heterogeneous information networks. In: Jensen, C.S., et al. (eds.) DASFAA 2021. LNCS, vol. 12681, pp. 158–173. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73194-6_12
Huang, Z., Zheng, Y., Cheng, R., Sun, Y., Mamoulis, N., Li, X.: Meta structure: computing relevance in large heterogeneous information networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1595–1604 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Li, X., Zhou, L., Kong, B., Wang, L. (2023). Influential Community Search Over Large Heterogeneous Information Networks. In: Meng, X., et al. Spatial Data and Intelligence. SpatialDI 2023. Lecture Notes in Computer Science, vol 13887. Springer, Cham. https://doi.org/10.1007/978-3-031-32910-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-031-32910-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-32909-8
Online ISBN: 978-3-031-32910-4
eBook Packages: Computer ScienceComputer Science (R0)