DiP-GNN: Discriminative Pre-Training of Graph Neural Networks

• URL: http://arxiv.org/abs/2209.07499v1
• Date: Thu, 15 Sep 2022 17:41:50 GMT
• Title: DiP-GNN: Discriminative Pre-Training of Graph Neural Networks
• Authors: Simiao Zuo, Haoming Jiang, Qingyu Yin, Xianfeng Tang, Bing Yin, Tuo Zhao
• Abstract summary: Graph neural network (GNN) pre-training methods have been proposed to enhance the power of GNNs. One popular pre-training method is to mask out a proportion of the edges, and a GNN is trained to recover them. In our framework, the graph seen by the discriminator better matches the original graph because the generator can recover a proportion of the masked edges.
• Score: 49.19824331568713
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph neural network (GNN) pre-training methods have been proposed to enhance the power of GNNs. Specifically, a GNN is first pre-trained on a large-scale unlabeled graph and then fine-tuned on a separate small labeled graph for downstream applications, such as node classification. One popular pre-training method is to mask out a proportion of the edges, and a GNN is trained to recover them. However, such a generative method suffers from graph mismatch. That is, the masked graph inputted to the GNN deviates from the original graph. To alleviate this issue, we propose DiP-GNN (Discriminative Pre-training of Graph Neural Networks). Specifically, we train a generator to recover identities of the masked edges, and simultaneously, we train a discriminator to distinguish the generated edges from the original graph's edges. In our framework, the graph seen by the discriminator better matches the original graph because the generator can recover a proportion of the masked edges. Extensive experiments on large-scale homogeneous and heterogeneous graphs demonstrate the effectiveness of the proposed framework.

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