Abstract
Temporal network embedding aims to generate a low-dimensional representation for the nodes in the temporal network. However, the existing works rarely pay attention to the effect of meso-dynamics. Only a few works consider the structural identity of the motif, while they do not consider the temporal relationship of the motif. In this paper, we mainly focus on a particular temporal motif: the temporal triad. We propose the Temporal Network Embedding with Motif Structural Features (MSTNE), a novel temporal network embedding method that preserves structural features, including structural identity and temporal relationship of the motif during the evolution of the network. The MSTNE samples the neighbor node based on the temporal triads and models the effects of different temporal triads using the Hawkes process. To distinguish the importance of different structural and temporal triads, we introduce the attention mechanism. We evaluate the performance of MSTNE on four real-world data sets. The experimental results demonstrate that MSTNE achieves the best performance compared to several state-of-the-art approaches in different tasks, including node classification, temporal link prediction, and temporal node recommendation.
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Acknowledgment
This work is supported by the National Key Research and Development Program of China (Grant No. 2016YFB1000304) and National Natural Science Foundation of China (Grant No. 1636208).
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Qiao, Z., Li, W., Li, Y. (2022). Temporal Network Embedding with Motif Structural Features. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13245. Springer, Cham. https://doi.org/10.1007/978-3-031-00123-9_53
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DOI: https://doi.org/10.1007/978-3-031-00123-9_53
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