Abstract
Temporal networks are networks that edges evolve over time. Network embedding is an important approach that aims at learning low-dimension latent representations of nodes while preserving the spatial-temporal features for temporal network analysis. In this paper, we propose a spatial-temporal higher-order graph convolutional network framework (ST-HN) for temporal network embedding. To capture spatial-temporal features, we develop a truncated hierarchical random walk sampling algorithm (THRW), which randomly samples the nodes from the current snapshot to the previous one. To capture hierarchical attributes, we improve upon the state-of-the-art approach, higher-order graph convolutional architectures, to be able to aggregate spatial features of different hops and temporal features of different timestamps with weight, which can learn mixed spatial-temporal feature representations of neighbors at various hops and snapshots and can well preserve the evolving behavior hierarchically. Extensive experiments on link prediction demonstrate the effectiveness of our model.
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Acknowledgements
This work has been supported by the Chongqing Graduate Research and Innovation Project (CYB19096), the Fundamental Research Funds for the Central Universities (XDJK2020D021), the Capacity Development Grant of Southwest University (SWU116007), the China Scholarship Council (202006990041), and the National Natural Science Foundation of China (61672435, 61732019, 61811530327).
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Mo, X., Pang, J., Liu, Z. (2020). Higher-Order Graph Convolutional Embedding for Temporal Networks. In: Huang, Z., Beek, W., Wang, H., Zhou, R., Zhang, Y. (eds) Web Information Systems Engineering – WISE 2020. WISE 2020. Lecture Notes in Computer Science(), vol 12342. Springer, Cham. https://doi.org/10.1007/978-3-030-62005-9_1
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DOI: https://doi.org/10.1007/978-3-030-62005-9_1
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