Its All Graph To Me: Foundational Topology Models with Contrastive Learning on Multiple Domains

Representations and embeddings of graph data have been essential in many domains of research. The principle benefit of learning such representations is that the pre-trained model can be fine-tuned on smaller datasets where data or labels are scarse. Existing models, however, are domain specific; for example a model trained on molecular graphs is fine-tuned on other molecular graphs. This means that in many application cases the choice of pre-trained model can be arbitrary, and novel domains may lack an appropriate pre-trained model. This is of particular issue where data is scarse, precluding traditional supervised methods. In this work we use adversarial contrastive learning to present a \method, a model pre-trained on many graph domains. We train the model only on topologies but include node labels in evaluation. We evaluate the efficacy of its learnt representations on various downstream tasks. Against baseline models pre-trained on single domains, as well as un-trained models and non-transferred models, we show that performance is equal or better using our single model. This includes when node labels are used in evaluation, where performance is consistently superior to single-domain or non-pre-trained models.