ServiceNow Research

Flaky Performances when Pretraining on Relational Databases


We explore the downstream task performances for graph neural network (GNN) self-supervised learning (SSL) methods trained on subgraphs extracted from relational databases (RDBs). Intuitively, this joint use of SSL and GNNs should allow to leverage more of the available data, which could translate to better results. However, we found that naively porting contrastive SSL techniques can cause ``negative transfer’’: linear evaluation on fixed representations from a pretrained model performs worse than on representations from the randomly-initialized model. Based on the conjecture that contrastive SSL conflicts with the message passing layers of the GNN, we propose InfoNode: a contrastive loss aiming to maximize the mutual information between a node’s initial- and final-layer representation. The primary empirical results support our conjecture and the effectiveness of InfoNode.

AAAI-23 Student Abstract and Poster Program
David Vazquez
David Vazquez
Manager of Research Programs

Manager of Research Programs at Research Management located at Montreal, QC, Canada.

Pierre-André Noël
Pierre-André Noël
Applied Research Scientist

Applied Research Scientist at Low Data Learning located at Montreal, QC, Canada.