Graph Neural Networks

Towards expansive and adaptive hard negative mining: Graph contrastive learning via subspace preserving
Graph Neural Networks (GNNs) have emerged as the predominant approach for analyzing graph data on the web and beyond. Contrastive …
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Homophily-enhanced Structure Learning for Graph Clustering
Graph clustering is a fundamental task in graph analysis, and recent advances in utilizing graph neural networks (GNNs) have shown …
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On the Effectiveness of Sampled Softmax Loss for Item Recommendation
The learning objective plays a fundamental role to build a recommender system. Most methods routinely adopt either pointwise (e.g., …
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Online Cross-Layer Knowledge Distillation on Graph Neural Networks with Deep Supervision
Graph neural networks (GNNs) have become one of the most popular research topics in both academia and industry communities for their …
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