Abstract
The problem of link prediction in dynamic heterogeneous information networks has been widely studied in recent years. The technique of network embedding has been proved extremely useful for link prediction. However, the existing methods lack the close combination between deep-level features and temporal features of networks, which affects the accuracy of prediction and makes it difficult to adapt to the dynamic networks. In this paper, a Smooth Evolution model for Network Embedding (called SENE) is proposed, which considers both deep-level features and temporal features to obtain the embedded representations of the network structure, and uses the transformer mechanism to effectively obtain the smooth evolution of network embedding. Also an SENE-based link prediction algorithm is proposed, which can effectively guarantee the accuracy of link prediction. The feasibility and effectiveness of the proposed key technologies are verified by experiments.
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Acknowledgment
This work is supported by the National Key R&D Program of China (2018YFB1003404) and the National Natural Science Foundation of China (61672142).
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Dong, H., Kou, Y., Shen, D., Nie, T. (2020). Link Prediction Based on Smooth Evolution of Network Embedding. In: Wang, G., Lin, X., Hendler, J., Song, W., Xu, Z., Liu, G. (eds) Web Information Systems and Applications. WISA 2020. Lecture Notes in Computer Science(), vol 12432. Springer, Cham. https://doi.org/10.1007/978-3-030-60029-7_41
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