Self-supervised graph representation learning using multi-scale subgraph views contrast

Graph representation learning has received widespread attention in recent years. Most of the existing graph representation learning methods are based on supervised learning and require the complete graph as input. It needs a lot of computation memory cost. Besides, real-world graph data lacks labels and the cost of manually labeling data is expensive. Self-supervised learning provides a potential solution for graph representation learning to address these issues. Recently, multi-scale and multi-level self-supervised contrastive methods have been successfully applied. But most of these methods operate on complete graph data. Although the subgraph contrastive strategy improves the shortcomings of the previous self-supervised contrastive learning method, these subgraph contrastive methods only use a single contrastive strategy, which cannot fully extract the information in the graph. To approach these problems, in this paper, we introduce a novel self-supervised contrastive framework for graph representation learning. We generate multi-subgraph views for all nodes by a mixed sampling method. Our method learns node representation by a multi-scale contrastive loss. Specifically, we employ two objectives called bootstrapping contrastive loss and node-level agreement contrastive loss to maximize the node agreement between different subgraph views of the same node. Extensive experiments prove that compared with the state-of-the-art graph representation learning methods, our method is superior to a range of existing models in node classification task and computation memory costs.