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Graph Neural Networks

4,495 papers

Papers per year

Papers

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods NIPS 2021
Learning Graph Cellular Automata NIPS 2021
Deconvolutional Networks on Graph Data NIPS 2021
Rethinking Graph Transformers with Spectral Attention NIPS 2021
Dirichlet Energy Constrained Learning for Deep Graph Neural Networks NIPS 2021
Disentangled Contrastive Learning on Graphs NIPS 2021
DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks NIPS 2021
Ultrahyperbolic Neural Networks NIPS 2021
NeuroMLR: Robust & Reliable Route Recommendation on Road Networks NIPS 2021
Reinforcement Learning Enhanced Explainer for Graph Neural Networks NIPS 2021
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks NIPS 2021
NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs NIPS 2021
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair NIPS 2021
Adaptive Diffusion in Graph Neural Networks NIPS 2021
Learning Semantic Representations to Verify Hardware Designs NIPS 2021
Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration NIPS 2021
Particle Cloud Generation with Message Passing Generative Adversarial Networks NIPS 2021
Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing NIPS 2021
Residual2Vec: Debiasing graph embedding with random graphs NIPS 2021
On the Power of Edge Independent Graph Models NIPS 2021
AutoGEL: An Automated Graph Neural Network with Explicit Link Information NIPS 2021
Recognizing Vector Graphics without Rasterization NIPS 2021
Not All Low-Pass Filters are Robust in Graph Convolutional Networks NIPS 2021
Multi-Scale Representation Learning on Proteins NIPS 2021
Neural Production Systems NIPS 2021