623 citations · 2.1k across the 30 of their papers we have counts for
5 papers · 1 filter
Pitfalls of Graph Neural Network Evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski +1
Semi-supervised node classification in graphs is a fundamental problem in graph mining, and the recently proposed graph neural networks (GNNs) have achieved unparalleled results on…
Predict then Propagate: Graph Neural Networks meet Personalized PageRank
Johannes Gasteiger, Aleksandar Bojchevski, Stephan Günnemann
Neural message passing algorithms for semi-supervised classification on graphs have recently achieved great success. However, for classifying a node these methods only consider nod…
Adversarial Attacks on Node Embeddings via Graph Poisoning
Aleksandar Bojchevski, Stephan Günnemann
The goal of network representation learning is to learn low-dimensional node embeddings that capture the graph structure and are useful for solving downstream tasks. However, despi…
Dual-Primal Graph Convolutional Networks
Federico Monti, Oleksandr Shchur, Aleksandar Bojchevski +3
In recent years, there has been a surge of interest in developing deep learning methods for non-Euclidean structured data such as graphs. In this paper, we propose Dual-Primal Grap…
NetGAN: Generating Graphs via Random Walks
Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner +1
We propose NetGAN - the first implicit generative model for graphs able to mimic real-world networks. We pose the problem of graph generation as learning the distribution of biased…