-
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...
08/29/2020 ∙ by Aleksandar Bojchevski, et al. ∙
0
∙
share
read it
-
Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
07/03/2020 ∙ by Aleksandar Bojchevski, et al. ∙
0
∙
share
read it
-
Certifiable Robustness to Graph Perturbations
Despite the exploding interest in graph neural networks there has been l...
10/31/2019 ∙ by Aleksandar Bojchevski, et al. ∙
0
∙
share
read it
-
Group Centrality Maximization for Large-scale Graphs
The study of vertex centrality measures is a key aspect of network analy...
10/30/2019 ∙ by Eugenio Angriman, et al. ∙
0
∙
share
read it
-
Pitfalls of Graph Neural Network Evaluation
Semi-supervised node classification in graphs is a fundamental problem i...
11/14/2018 ∙ by Oleksandr Shchur, et al. ∙
0
∙
share
read it
-
Personalized Embedding Propagation: Combining Neural Networks on Graphs with Personalized PageRank
Neural message passing algorithms for semi-supervised classification on ...
10/14/2018 ∙ by Johannes Klicpera, et al. ∙
0
∙
share
read it
-
Dual-Primal Graph Convolutional Networks
In recent years, there has been a surge of interest in developing deep l...
06/03/2018 ∙ by Federico Monti, et al. ∙
0
∙
share
read it
-
NetGAN: Generating Graphs via Random Walks
We propose NetGAN - the first implicit generative model for graphs able ...
03/02/2018 ∙ by Aleksandar Bojchevski, et al. ∙
0
∙
share
read it
-
Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Methods that learn representations of graph nodes play a critical role i...
07/12/2017 ∙ by Aleksandar Bojchevski, et al. ∙
0
∙
share
read it
