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arXiv · 2102.12380

Pre-Training on Dynamic Graph Neural Networks

Ke-jia Chen·Jiajun Zhang·Linpu Jiang·Yunyun Wang·Yuxuan Dai
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Abstract

The pre-training on the graph neural network model can learn the general features of large-scale networks or networks of the same type by self-supervised methods, which allows the model to work even when node labels are missing. However, the existing pre-training methods do not take network evolution into consideration. This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN), which uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph. The method includes two steps: 1) dynamic sub-graph sampling, and 2) pre-training with dynamic attributed graph generation task. Comparative experiments on three realistic dynamic network datasets show that the proposed method achieves the best results on the link prediction fine-tuning task.

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Ke-jia Chen, Jiajun Zhang, Linpu Jiang, Yunyun Wang, Yuxuan Dai. 2021-02-24. Pre-Training on Dynamic Graph Neural Networks. https://arxiv.org/abs/2102.12380

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