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Efficient Robustness Certificates for Discrete Data: Sparsity-Aware Randomized Smoothing for Graphs, Images and More
Existing techniques for certifying the robustness of models for discrete...
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Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
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Certifiable Robustness to Graph Perturbations
Despite the exploding interest in graph neural networks there has been l...
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Group Centrality Maximization for Large-scale Graphs
The study of vertex centrality measures is a key aspect of network analy...
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Pitfalls of Graph Neural Network Evaluation
Semi-supervised node classification in graphs is a fundamental problem i...
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Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank
Neural message passing algorithms for semi-supervised classification on ...
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Dual-Primal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep l...
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NetGAN: Generating Graphs via Random Walks
We propose NetGAN - the first implicit generative model for graphs able ...
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Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Methods that learn representations of graph nodes play a critical role i...
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