Pre-Training on Dynamic Graph Neural Networks

• URL: http://arxiv.org/abs/2102.12380v1
• Date: Wed, 24 Feb 2021 16:06:32 GMT
• Title: Pre-Training on Dynamic Graph Neural Networks
• Authors: Jiajun Zhang, Kejia Chen, Yunyun Wang
• Abstract summary: This paper proposes a pre-training method on dynamic graph neural networks (PT-DGNN) It uses dynamic attributed graph generation tasks to simultaneously learn the structure, semantics, and evolution features of the graph.
• Score: 26.139844652756334
• License: http://creativecommons.org/licenses/by/4.0/
• 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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