
A Survey on The Expressive Power of Graph Neural Networks
Graph neural networks (GNNs) are effective machine learning models for v...
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Learning to Drop: Robust Graph Neural Network via Topological Denoising
Graph Neural Networks (GNNs) have shown to be powerful tools for graph a...
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XAI for Graphs: Explaining Graph Neural Network Predictions by Identifying Relevant Walks
Graph Neural Networks (GNNs) are a popular approach for predicting graph...
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Graph Neural Ordinary Differential Equations
We extend the framework of graph neural networks (GNN) to continuous tim...
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A Deep Graph Neural Networks Architecture Design: From Global Pyramidlike Shrinkage Skeleton to Local Topology Link Rewiring
Expressivity plays a fundamental role in evaluating deep neural networks...
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From Graph LowRank Global Attention to 2FWL Approximation
Graph Neural Networks (GNNs) are known to have an expressive power bound...
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Breaking the Expressive Bottlenecks of Graph Neural Networks
Recently, the WeisfeilerLehman (WL) graph isomorphism test was used to ...
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Topological Graph Neural Networks
Graph neural networks (GNNs) are a powerful architecture for tackling graph learning tasks, yet have been shown to be oblivious to eminent substructures, such as cycles. We present TOGL, a novel layer that incorporates global topological information of a graph using persistent homology. TOGL can be easily integrated into any type of GNN and is strictly more expressive in terms of the Weisfeiler–Lehman test of isomorphism. Augmenting GNNs with our layer leads to beneficial predictive performance, both on synthetic data sets, which can be trivially classified by humans but not by ordinary GNNs, and on realworld data.
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