Abstract
Ranking of vertices is an important part of social network analysis. However, thanks to the enormous growth of real-world networks, the global ranking of vertices on a large scale does not provide easily comparable results. On the other hand, the ranking can provide clear results on a local scale and also in heterogeneous networks where we need to work with vertices of different types. In this paper, we present a method of ranking objects in a community which is closely related to the analysis of heterogeneous information networks. Our method assumes that the community is a set of several groups of objects of different types where each group, so-called object pool, contains objects of the same type. These community object pools can be connected and ordered to the chain of influencers, and ranking can be applied to this structure. Based on the chain of influencers, the heterogeneous network can be converted to a multipartite graph. In our approach, we show how to rank vertices of the community using the mutual influence of community object pools. In our experiments, we worked with a computer science research community. Objects of this domain contain authors, papers (articles), topics (keywords), and years of publications.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This data is freely available at http://dblp.org/xml/release/
- 2.
References
Chen, J., Dai, W., Sun, Y., Dy, J.: Clustering and ranking in heterogeneous information networks via gamma-poisson model. In: Proceedings of the 2015 SIAM International Conference on Data Mining, pp. 424–432. SIAM (2015)
Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)
Han, J., Sun, Y., Yan, X., Yu, P.S.: Mining heterogeneous information networks. In: Tutorial at the 2010 ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2010), Washington, DC (2010)
Huang, H., Zubiaga, A., Ji, H., Deng, H., Wang, D., Le, H.K., Abdelzaher, T.F., Han, J., Leung, A., Hancock, J.P., et al.: Tweet ranking based on heterogeneous networks. In: COLING, pp. 1239–1256 (2012)
Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery And Data Mining, pp. 538–543. ACM (2002)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM (JACM) 46(5), 604–632 (1999)
Li, X., Ng, M., Ye, Y.: Multirank: co-ranking for objects and relations in multi-relational data. In: 17th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD-2011), pp. 1217–1225 (2011)
Liu, X., Yu, Y., Guo, C., Sun, Y.: Meta-path-based ranking with pseudo relevance feedback on heterogeneous graph for citation recommendation. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 121–130. ACM (2014)
Liu, X., Yu, Y., Guo, C., Sun, Y., Gao, L.: Full-text based context-rich heterogeneous network mining approach for citation recommendation. In: Proceedings of the 14th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 361–370. IEEE Press (2014)
Ni, J., Tong, H., Fan, W., Zhang, X.: Inside the atoms: ranking on a network of networks. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1356–1365. ACM (2014)
Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: bringing order to the web. Technical report, Stanford InfoLab (1999)
Shi, C., Kong, X., Huang, Y., Philip, S.Y., Wu, B.: Hetesim: a general framework for relevance measure in heterogeneous networks. IEEE Trans. Knowl. Data Eng. 26(10), 2479–2492 (2014)
Shi, C., Li, Y., Philip, S.Y., Wu, B.: Constrained-meta-path-based ranking in heterogeneous information network. Knowl. Inf. Syst. 49(2), 719–747 (2016)
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 (2017)
Soulier, L., Jabeur, L.B., Tamine, L., Bahsoun, W.: On ranking relevant entities in heterogeneous networks using a language-based model. J. Am. Soc. Inf. Sci. Technol. 64(3), 500–515 (2013)
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)
Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., Rankclus, T.: Integrating clustering with ranking for heterogeneous information network analysis. In: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology, pp. 565–576. ACM (2009)
Tsai, M.-H., Aggarwal, C., Huang, T.: Ranking in heterogeneous social media. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 613–622. ACM (2014)
Yu, X., Ren, X., Sun, Y., Gu, Q., Sturt, B., Khandelwal, U., Norick, B., Han, J.: Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining, pp. 283–292. ACM (2014)
Zhou, D., Orshanskiy, S.A., Zha, H., Giles, C.L.: Co-ranking authors and documents in a heterogeneous network. In: Seventh IEEE International Conference on Data Mining, ICDM 2007, pp. 739–744. IEEE (2007)
Acknowledgement
This work was supported by the Czech Science Foundation under the grant no. GA15-06700S, and by the projects SP2017/100 and SP2017/85 of the Student Grant System, VŠB-Technical University of Ostrava.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Drazdilova, P., Konecny, J., Kudelka, M. (2017). Chain of Influencers: Multipartite Intra-community Ranking. In: Cao, Y., Chen, J. (eds) Computing and Combinatorics. COCOON 2017. Lecture Notes in Computer Science(), vol 10392. Springer, Cham. https://doi.org/10.1007/978-3-319-62389-4_50
Download citation
DOI: https://doi.org/10.1007/978-3-319-62389-4_50
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-62388-7
Online ISBN: 978-3-319-62389-4
eBook Packages: Computer ScienceComputer Science (R0)