Towards Generalised Pre-Training of Graph Models

• URL: http://arxiv.org/abs/2311.03976v3
• Date: Tue, 14 May 2024 12:46:35 GMT
• Title: Towards Generalised Pre-Training of Graph Models
• Authors: Alex O. Davies, Riku W. Green, Nirav S. Ajmeri, Telmo M. Silva Filho,
• Abstract summary: We present Topology Only Pre-Training, a graph pre-training method based on node and edge feature exclusion. Models show positive transfer on evaluation datasets from multiple domains, including domains not present in pre-training data.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The principal benefit of unsupervised representation learning is that a pre-trained model can be fine-tuned where data or labels are scarce. Existing approaches for graph representation learning are domain specific, maintaining consistent node and edge features across the pre-training and target datasets. This has precluded transfer to multiple domains. In this work we present Topology Only Pre-Training, a graph pre-training method based on node and edge feature exclusion. Separating graph learning into two stages, topology and features, we use contrastive learning to pre-train models over multiple domains. These models show positive transfer on evaluation datasets from multiple domains, including domains not present in pre-training data. On 75% of experiments, ToP models perform significantly ($P \leq 0.01$) better than a supervised baseline. These results include when node and edge features are used in evaluation, where performance is significantly better on 85.7% of tasks compared to single-domain or non-pre-trained models. We further show that out-of-domain topologies can produce more useful pre-training than in-domain. We show better transfer from non-molecule pre-training, compared to molecule pre-training, on 79% of molecular benchmarks.

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