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We explore the downstream task performances for graph neural network (GNN)self-supervised learning (SSL) methods trained on subgraphs extracted fromrelational databases (RDBs). Intuitively, this joint use of SSL and GNNs shouldallow to leverage more of the available data, which could translate to betterresults. However, we found that naively porting contrastive SSL techniques cancause ``negative transfer'': linear evaluation on fixed representations from apretrained model performs worse than on representations from therandomly-initialized model. Based on the conjecture that contrastive SSLconflicts with the message passing layers of the GNN, we propose InfoNode: acontrastive loss aiming to maximize the mutual information between a node'sinitial- and final-layer representation. The primary empirical results supportour conjecture and the effectiveness of InfoNode.

Flaky Performances when Pretraining on Relational Databases