Self-supervised Graph Representation Learning via Bootstrapping
This addresses the need for unsupervised graph representation learning in domains like social networks or bioinformatics, though it appears incremental as it builds on existing self-supervised techniques.
The paper tackles the problem of graph representation learning without relying on labels or negative samples by proposing a self-supervised method called deep graph bootstrapping (DGB), which uses two neural networks to learn from each other via graph augmentations, and experiments show it outperforms state-of-the-art methods on benchmark datasets.
Graph neural networks~(GNNs) apply deep learning techniques to graph-structured data and have achieved promising performance in graph representation learning. However, existing GNNs rely heavily on enough labels or well-designed negative samples. To address these issues, we propose a new self-supervised graph representation method: deep graph bootstrapping~(DGB). DGB consists of two neural networks: online and target networks, and the input of them are different augmented views of the initial graph. The online network is trained to predict the target network while the target network is updated with a slow-moving average of the online network, which means the online and target networks can learn from each other. As a result, the proposed DGB can learn graph representation without negative examples in an unsupervised manner. In addition, we summarize three kinds of augmentation methods for graph-structured data and apply them to the DGB. Experiments on the benchmark datasets show the DGB performs better than the current state-of-the-art methods and how the augmentation methods affect the performances.