NNC-GCN: Neighbours-to-Neighbours Contrastive Graph Convolutional Network for Semi-Supervised Classification

Abstract

Contrastive learning (CL) is a popular learning paradigm in deep learning, which uses contrastive principle to learn low-dimensional embeddings, and has been applied in Graph Neural Networks (GNNs) successfully. Existing works of contrastive multi-view GNNs usually focus on point-to-point contrastive learning strategies. However, they neglect the local information in neighbors, which brings isolated positive samples. The quality of selected positive samples is hard to evaluate, and these samples may lead to invalid contrastiveness. Therefore, we propose a simple and efficient neighbors-to-neighbors contrastive graph neural network (NNC-GCN), which constructs a consistent multi-view by using the topologies of original input graphs. Moreover, we raise a new learning problem of unlabeled data base on these constructed multi-view topologies and propose a loss function NNC-InfoNCE to guide its learning process. The NNC-InfoNCE is an improved version of InfoNCE, which can be adapted to neighborhood-level contrast learning. Specifically, the neighborhoods and the remaining nodes of the selected anchor are weighted and treated as positive and negative sample sets. The experimental results show that our method is effective on public benchmark datasets.