Abstract
Link prediction on temporal networks has a wide range of applications, such as facilitating individual relationship mining, user recommendation, and user behavior analysis. The traditional link prediction methods on temporal network only considered the structure of single-relational networks, which ignored the diversity of network link types and the influence between different link types. This paper proposes a temporal multi-relational network link prediction method combining personalized time interval and node embedding. Firstly, the node embedding is generated according to the structure of target network and auxiliary network which overcomes the defect of single network information sparseness; then, considering the diversity of link types, we construct the relationship formation sequence based on personalized time interval and the influence between different relationships for each link; next, the relationship formation sequence is modeled based on the Hawkes process, which takes product of the node embedding as the initial value to calculate the possibility of link formation. The method captures the dynamic characteristics and multi-relational property of the network, which is helpful to improve the accuracy of link prediction. Experimental results find that the proposed method has better performance and can be applied to large-scale networks.
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References
Dai, C., Chen, L., Li, B.: Link prediction in multi-relational networks based on relational similarity. Inf. Sci. 394–395, 198–216 (2017)
Newman, M.E.J.: Clustering and preferential attachment in growing networks. Phys. Rev. E 64(2), 025102 (2001)
Liben Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. In: ACM CIKM, Louisiana, USA, pp. 1019–1031 (2003)
Yu, W., Cheng, W., Aggarwal, C.C., et al.: Link prediction with spatial and temporal consistency in dynamic networks. In: Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia, pp. 3343–3349 (2017)
Li, J., Cheng, K., Wu, L., et al.: Streaming link prediction on dynamic attributed networks. In: ACM WSDM, Marina Del Rey, USA, pp. 369–377 (2018)
Ozcan, A., Oguducu, S.G.: Link prediction in evolving heterogeneous networks using the NARX neural networks. Knowl. Inf. Syst. 55(2), 333–360 (2018)
Huang, Z., Mamoulis, N.: Heterogeneous information network embedding for meta path based proximity. CoRR abs/1701.05291 (2017)
Li, Y., Li, C., Chen, W.: Research on influence ranking of chinese movie heterogeneous network based on PageRank algorithm. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 344–356. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_32
Sett, N., Basu, S., Nandi, S., et al.: Temporal link prediction in multi-relational network. World Wide Web Web Inf. Syst. 21(2), 395–419 (2018)
Liu, L., Li, X., Cheung, W.K., et al.: A structural representation learning for multi-relational networks. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia, pp. 4047–4053 (2017)
Wang, Y., Gemulla, R., Li, H.: On multi-relational link prediction with bilinear models. In: Proceedings of the 32th AAAI Conference on Artificial Intelligence, New Orleans, USA, pp. 4227–4234 (2018)
Tang, J., Qu, M., Wang, M., et al.: LINE: large-scale information network embedding. In: International Conference on World Wide Web, Florence, Italy, pp. 1067–1077 (2015)
Yu, W., Aggarwal, C.C., Wang, W.: Modeling co-evolution across multiple networks. In: ACM SDM, San Diego, USA, pp. 675–683 (2018)
Cao, Z., Wang, L.: Link prediction via subgraph embedding-based convex matrix completion. In: Proceedings of the 32th AAAI Conference on Artificial Intelligence, New Orleans, USA, pp. 2803–2810 (2018)
Lemonnier, R., Scaman, K., Kalogeratos, A.: Multivariate hawkes processes for large-scale inference. In: Proceedings of the 31th AAAI Conference on Artificial Intelligence, California, USA, pp. 2168–2174 (2017)
Acknowledgment
This work is supported by the National Key R&D Program of China (2018YFB1003404), the National Natural Science Foundation of China (61472070, 61672142, 61602103) and the Program of China Scholarships Council (201806085016).
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Liu, Y., Shen, D., Kou, Y., Nie, T. (2019). Link Prediction Based on Node Embedding and Personalized Time Interval in Temporal Multi-relational Network. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_40
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DOI: https://doi.org/10.1007/978-3-030-30952-7_40
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