ConGraT: Self-Supervised Contrastive Pretraining for Joint Graph and Text Embeddings
This addresses the challenge of balancing text and graph representations in text-attributed graphs for applications like social network analysis, though it is incremental as it builds on existing contrastive learning techniques.
The authors tackled the problem of learning on text-attributed graphs by proposing ConGraT, a self-supervised method that jointly learns separate representations for texts and nodes, outperforming baselines on tasks like node classification and link prediction.
Learning on text-attributed graphs (TAGs), in which nodes are associated with one or more texts, has been the subject of much recent work. However, most approaches tend to make strong assumptions about the downstream task of interest, are reliant on hand-labeled data, or fail to equally balance the importance of both text and graph representations. In this work, we propose Contrastive Graph-Text pretraining (ConGraT), a general, self-supervised approach for jointly learning separate representations of texts and nodes in a TAG. Our method trains a language model (LM) and a graph neural network (GNN) to align their representations in a common latent space using a batch-wise contrastive learning objective inspired by CLIP. We further propose an extension to the CLIP objective that leverages graph structure to incorporate information about inter-node similarity. Extensive experiments demonstrate that ConGraT outperforms baselines on various downstream tasks, including node and text category classification, link prediction, and language modeling. Finally, we present an application of our method to community detection in social graphs, which enables finding more textually grounded communities, rather than purely graph-based ones. Code and certain datasets are available at https://github.com/wwbrannon/congrat.