Abstract
Studies have demonstrated that real-world data can be modeled as a heterogeneous information network (HIN) composed of multiple types of entities and relationships. Similarity search is a basic operation requiring many problems in HINs. Similarity measures can be used in various applications, including friend recommendation, link prediction, and online advertising. However, most existing similarity measures only consider meta path. Complex semantic meaning cannot be expressed through meta path. In this paper, we study the similarity search problem of complex semantics meaning between two HIN objects. In order to solve the problem, we use meta graphs to express the semantic meaning between objects. The advantage of meta graphs is that it can describe the complex semantic meaning between two HIN objects. And we first define a new meta graph-based relation similarity measure, GraphSim, which is to measure the similarity between objects in HINs, then we propose a similarity search framework based on GraphSim. The experiments with real-world datasets from DBLP demonstrated the effectiveness of our approach.
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References
Chakrabarti, S.: Dynamic personalized pagerank in entity-relation graphs. In: The Web Conference, pp. 571–580 (2007)
Han, J.: Mining heterogeneous information networks by exploring the power of links. In: Gama, J., Costa, V.S., Jorge, A.M., Brazdil, P.B. (eds.) DS 2009. LNCS (LNAI), vol. 5808, pp. 13–30. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04747-3_2
Huang, Z., Zheng, Y., Cheng, R., Sun, Y., Mamoulis, N., Li, X.: Meta structure: computing relevance in large heterogeneous information networks, pp. 1595–1604 (2016)
Jarvelin, K., Kekalainen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. 20(4), 422–446 (2002)
Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity, pp. 538–543 (2002)
Ji, M., Sun, Y., Danilevsky, M., Han, J., Gao, J.: Graph regularized transductive classification on heterogeneous information networks. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010. LNCS (LNAI), vol. 6321, pp. 570–586. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15880-3_42
Ley, M.: The DBLP computer science bibliography: evolution, research issues, perspectives. In: Laender, A.H.F., Oliveira, A.L. (eds.) SPIRE 2002. LNCS, vol. 2476, pp. 1–10. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45735-6_1
Libennowell, D., Kleinberg, J.M.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58(7), 1019–1031 (2007)
Meng, C., Cheng, R., Maniu, S., Senellart, P., Zhang, W.: Discovering meta-paths in large heterogeneous information networks. In: The Web Conference, pp. 754–764 (2015)
Shi, C., Li, Y., Zhang, J., Sun, Y., Yu, P.S.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2015)
Sun, Y., Han, J.: Mining heterogeneous information networks: a structural analysis approach. Sigkdd Explor. 14(2), 20–28 (2013)
Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: meta path-based top-k similarity search in heterogeneous information networks. Very Large Data Bases 4(11), 992–1003 (2011)
Sun, Y., Han, J., Zhao, P., Yin, Z., Cheng, H., Wu, T.: RankClus: integrating clustering with ranking for heterogeneous information network analysis, pp. 565–576 (2009)
Wang, C., et al.: RelSim: relation similarity search in schema-rich heterogeneous information networks, pp. 621–629 (2016)
Wang, S., Xie, S., Zhang, X., Li, Z., He, Y.: Coranking the future influence of multiobjects in bibliographic network through mutual reinforcement. ACM Trans. Intell. Syst. Technol. 7(4), 1–28 (2016)
Xiang, L., Zhao, G., Li, Q., Hao, W., Li, F.: TUMK-ELM: a fast unsupervised heterogeneous data learning approach. IEEE Access 6, 35305–35315 (2018)
Xiong, Y., Zhu, Y., Yu, P.S.: Top-k similarity join in heterogeneous information networks. IEEE Trans. Knowl. Data Eng. 27(6), 1710–1723 (2015)
Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: SCAN: a structural clustering algorithm for networks, pp. 824–833 (2007)
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant 61873089, Grant 61572180, and in part by the China National Key R&D Program during the 13th Five-year Plan Period under Grant 2018YFC0910405.
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Chen, X., Jiang, Y., Wu, Y., Wei, X., Lu, X. (2020). A Meta Graph-Based Top-k Similarity Measure for Heterogeneous Information Networks. In: Huang, DS., Premaratne, P. (eds) Intelligent Computing Methodologies. ICIC 2020. Lecture Notes in Computer Science(), vol 12465. Springer, Cham. https://doi.org/10.1007/978-3-030-60796-8_39
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DOI: https://doi.org/10.1007/978-3-030-60796-8_39
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