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GNNExplainer: Generating Explanations for Graph Neural Networks.

2019 · PubMed

Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs. GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating …

How Powerful is Implicit Denoising in Graph Neural Networks

2022 · arXiv (Cornell University)

Graph Neural Networks (GNNs), which aggregate features from neighbors, are widely used for graph-structured data processing due to their powerful representation learning capabilities. It is generally believed that GNNs can implicitly remove the non-predictive noises. …

Graph Convolutional Neural Networks for Web-Scale Recommender Systems

2018

Recent advancements in deep neural networks for graph-structured data have led to state-of-the-art performance on recommender system benchmarks. However, making these methods practical and scalable to web-scale recommendation tasks with billions of items and hundreds …

Hierarchical Graph Representation Learning with Differentiable Pooling

2018 · arXiv (Cornell University)

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods …