Abstract
Representation learning on graphs has recently attracted a lot of interest with graph convolutional networks (GCN) achieving state-of-the-art performance in many graph mining tasks. However, most of existing methods mainly focus on static graphs while ignoring the fact that real-world graphs may be dynamic in nature. Although a few recent studies have gone a step further to incorporate sequence modeling (e.g., RNN) with the GCN framework, they fail to capture the dynamism of graph structural (i.e., spatial) information over time. In this paper, we propose a Dynamic Graph Convolutional Network (DynGCN) that performs spatial and temporal convolutions in an interleaving manner along with a model adapting mechanism that updates model parameters to adapt to new graph snapshots. The model is able to extract both structural dynamism and temporal dynamism on dynamic graphs. We conduct extensive experiments on several real-world datasets for link prediction and edge classification tasks. Results show that DynGCN outperforms state-of-the-art methods.
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Acknowledgements
This work was supported by NSFC under grant 61932001, 61961130390. This work was also supported by Beijing Academy of Artificial Intelligence (BAAI).
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Li, J., Liu, Y., Zou, L. (2020). DynGCN: A Dynamic Graph Convolutional Network Based on Spatial-Temporal Modeling. 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_7
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