提出非对比加权双部图链接预测模型，提升工业与电商场景下的推荐效果。
WBT-BGRL: A Non-Contrastive Weighted Bipartite Link Prediction Model for Inductive Learning
- 采用双GCN编码器的非对比学习框架，引入加权三元组损失增强训练
- 在工业与电商数据集上表现优于现有方法，加权预训练显著提升性能
- 适合需要高效、可扩展的双部图链接预测任务，如推荐系统
双部图中的链接预测对推荐系统和故障检测等应用至关重要，但研究远不如单部图充分。对比方法因负样本采样效率低且存在偏差，而非对比方法仅依赖正样本。现有模型在拟合设置下表现良好，但在归纳、加权及双部场景下的有效性尚未验证。为此，我们提出加权双部三元组自举图嵌入（WBT-BGRL），一种非对比框架，通过新颖的加权机制改进自举学习中的三元组损失。该模型采用双部结构与双GCN编码器，在真实数据集（工业与电商）上与改进的先进模型（T-BGRL、BGRL、GBT、CCA-SSG）对比，结果表明性能具有竞争力，尤其在预训练阶段引入加权后优势更明显，凸显了加权非对比学习在双部图归纳链接预测中的价值。
原文摘要 · Abstract (English)
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.
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