Abstract
In this paper, we propose a new approach named D2NE, short for Deep Dynamic Network Embedding, to learn the vertex representations for dynamic networks. The algorithm utilizes the graph attention mechanism to refresh embeddings efficiently, in which each update associate with local information only. To address the missing data, which is a common phenomenon in real-world networks, we model the auxiliary side information to capture more information on vertex relations. D2NE is highly efficient to refresh embeddings for dynamic networks, even with standard real-world sparse networks. We conduct extensive experiments on several real-world dynamic networks to validate the performance of D2NE in the link prediction task. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our D2NE method.
C. Kong and B. Chen—The two authors contributed equally to this work.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China Youth Fund under Grant No. 61902001 and Initial Scientific Research Fund of Introduced Talents in Anhui Polytechnic University under Grant No. 2017YQQ015.
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Kong, C., Chen, B., Li, S., Zhou, Q., Wang, D., Zhang, L. (2020). D2NE: Deep Dynamic Network Embedding. In: Yang, X., Wang, CD., Islam, M.S., Zhang, Z. (eds) Advanced Data Mining and Applications. ADMA 2020. Lecture Notes in Computer Science(), vol 12447. Springer, Cham. https://doi.org/10.1007/978-3-030-65390-3_14
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DOI: https://doi.org/10.1007/978-3-030-65390-3_14
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