Graph Representation Learning With Adaptive Metric

Contrastive learning has been widely used in graph representation learning, which extracts node or graph representations by contrasting positive and negative node pairs. It requires node representations (embeddings) to reflect their correlations in topology, increasing the similarities between an anchor node and its positive nodes, or reducing the similarities with its negative nodes in embedding space. However, most existing contrastive models measure similarities through a fixed metric that equally scores all sample pairs in a specific feature space, but ignores the varieties of node attributes and network topologies. Moreover, these fixed metrics are always defined explicitly and manually, which makes them unsuitable for applying to all graphs and networks. To solve these problems, we propose a novel graph representation learning model with an adaptive metric, called GRAM, which produces appropriate similarity scores of node pairs according to the different significance of each dimension in their embedding vectors and adaptive metrics based on data distribution. With these scores, it is better to train a graph encoder and obtain representative embeddings. Experimental results show that GRAM has strong competitiveness in multiple tasks.

Automated summary

Contrastive learning is widely used in graph representation learning, but most existing models ignore the diversity of node attributes and network topologies. To address this, we propose a novel graph representation learning model called GRAM, which uses an adaptive metric to generate appropriate similarity scores for node pairs based on the significance of each dimension in their embedding vectors and data distribution. Experimental results demonstrate that GRAM is highly competitive in multiple tasks.