
Design Space for Graph Neural Networks
The rapid evolution of Graph Neural Networks (GNNs) has led to a growing...
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Direct Multihop Attention based Graph Neural Network
Introducing selfattention mechanism in graph neural networks (GNNs) ach...
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Learning to Simulate Complex Physics with Graph Networks
Here we present a general framework for learning simulation, and provide...
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Hyperbolic Graph Convolutional Neural Networks
Graph convolutional neural networks (GCNs) embed nodes in a graph into E...
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Improving Graph Attention Networks with Large Marginbased Constraints
Graph Attention Networks (GATs) are the stateoftheart neural architec...
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Neural Execution of Graph Algorithms
Graph Neural Networks (GNNs) are a powerful representational tool for so...
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Positionaware Graph Neural Networks
Learning node embeddings that capture a node's position within the broad...
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RedundancyFree Computation Graphs for Graph Neural Networks
Graph Neural Networks (GNNs) are based on repeated aggregations of infor...
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GNN Explainer: A Tool for Posthoc Explanation of Graph Neural Networks
Graph Neural Networks (GNNs) are a powerful tool for machine learning on...
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Hierarchical Graph Representation Learning with Differentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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Hierarchical Graph Representation Learning withDifferentiable Pooling
Recently, graph neural networks (GNNs) have revolutionized the field of ...
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Graph Convolutional Policy Network for GoalDirected Molecular Graph Generation
Generating novel graph structures that optimize given objectives while o...
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Graph Convolutional Neural Networks for WebScale Recommender Systems
Recent advancements in deep neural networks for graphstructured data ha...
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GraphRNN: A Deep Generative Model for Graphs
Modeling and generating graphs is fundamental for studying networks in b...
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Inductive Representation Learning on Large Graphs
Lowdimensional embeddings of nodes in large graphs have proved extremel...
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Rex Ying
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