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Graph Neural Networks

4,522 papers

Papers per year

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Papers

Scalable Graph Neural Networks via Bidirectional Propagation NIPS 2020
Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs NIPS 2020
Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations NIPS 2020
Random Walk Graph Neural Networks NIPS 2020
Path Integral Based Convolution and Pooling for Graph Neural Networks NIPS 2020
Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding NIPS 2020
Design Space for Graph Neural Networks NIPS 2020
Discovering Symbolic Models from Deep Learning with Inductive Biases NIPS 2020
Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting NIPS 2020
Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies NIPS 2020
Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting NIPS 2020
Digraph Inception Convolutional Networks NIPS 2020
DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling NIPS 2020
Hybrid Models for Learning to Branch NIPS 2020
GCN meets GPU: Decoupling “When to Sample” from “How to Sample” NIPS 2020
Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks NIPS 2020
Handling Missing Data with Graph Representation Learning NIPS 2020
Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings NIPS 2020
Parameterized Explainer for Graph Neural Network NIPS 2020
Universal Function Approximation on Graphs NIPS 2020
Graph Stochastic Neural Networks for Semi-supervised Learning NIPS 2020
Factorizable Graph Convolutional Networks NIPS 2020
Strongly Incremental Constituency Parsing with Graph Neural Networks NIPS 2020
Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings NIPS 2020
Graph Random Neural Networks for Semi-Supervised Learning on Graphs NIPS 2020