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Graph Neural Networks

4,522 papers

Papers per year

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Papers

GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training ICML 2021
Learning Node Representations Using Stationary Flow Prediction on Large Payment and Cash Transaction Networks ICML 2021
GRAND: Graph Neural Diffusion ICML 2021
A Unified Lottery Ticket Hypothesis for Graph Neural Networks ICML 2021
Lipschitz normalization for self-attention layers with application to graph neural networks ICML 2021
Graph Mixture Density Networks ICML 2021
GNNAutoScale: Scalable and Expressive Graph Neural Networks via Historical Embeddings ICML 2021
Let’s Agree to Degree: Comparing Graph Convolutional Networks in the Message-Passing Framework ICML 2021
A Collective Learning Framework to Boost GNN Expressiveness for Node Classification ICML 2021
Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity ICML 2021
Interpretable Stability Bounds for Spectral Graph Filters ICML 2021
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More ICML 2021
Training Graph Neural Networks with 1000 Layers ICML 2021
Information Obfuscation of Graph Neural Networks ICML 2021
Generative Causal Explanations for Graph Neural Networks ICML 2021
Stochastic Iterative Graph Matching ICML 2021
Elastic Graph Neural Networks ICML 2021
Symmetric Spaces for Graph Embeddings: A Finsler-Riemannian Approach ICML 2021
Improving Breadth-Wise Backpropagation in Graph Neural Networks Helps Learning Long-Range Dependencies. ICML 2021
Controlling Graph Dynamics with Reinforcement Learning and Graph Neural Networks ICML 2021
Towards Open Ad Hoc Teamwork Using Graph-based Policy Learning ICML 2021
Principled Simplicial Neural Networks for Trajectory Prediction ICML 2021
E(n) Equivariant Graph Neural Networks ICML 2021
Equivariant message passing for the prediction of tensorial properties and molecular spectra ICML 2021
Directed Graph Embeddings in Pseudo-Riemannian Manifolds ICML 2021