WBT-BGRL: A Non-Contrastive Weighted Bipartite Link Prediction Model for Inductive Learning

TL;DR

This paper introduces WBT-BGRL, a novel non-contrastive, weighted bipartite link prediction model that improves inductive learning in bipartite graphs by leveraging a triplet loss with a new weighting mechanism.

Contribution

The paper proposes WBT-BGRL, a non-contrastive bipartite link prediction framework with a novel weighting mechanism in triplet loss, tailored for inductive, weighted bipartite graphs.

Findings

WBT-BGRL achieves competitive results on real-world datasets.

Weighted pretraining enhances model performance.

Non-contrastive approach is effective for inductive bipartite link prediction.

Abstract

Link prediction in bipartite graphs is crucial for applications like recommendation systems and failure detection, yet it is less studied than in monopartite graphs. Contrastive methods struggle with inefficient and biased negative sampling, while non-contrastive approaches rely solely on positive samples. Existing models perform well in transductive settings, but their effectiveness in inductive, weighted, and bipartite scenarios remains untested. To address this, we propose Weighted Bipartite Triplet-Bootstrapped Graph Latents (WBT-BGRL), a non-contrastive framework that enhances bootstrapped learning with a novel weighting mechanism in the triplet loss. Using a bipartite architecture with dual GCN encoders, WBT-BGRL is evaluated against adapted state-of-the-art models (T-BGRL, BGRL, GBT, CCA-SSG). Results on real-world datasets (Industry and E-commerce) show competitive performance, especially when weighting is applied during pretraining-highlighting the value of weighted, non-contrastive learning for inductive link prediction in bipartite graphs.