1k citations · 4.2k across the 120 of their papers we have counts for
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Multi-Source Neural Variational Inference
Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Learning from multiple sources of information is an important problem in machine-learning research. The key challenges are learning representations and formulating inference method…
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…
Mining Contrasting Quasi-Clique Patterns
Roberto Alonso, Stephan Günnemann
Mining dense quasi-cliques is a well-known clustering task with applications ranging from social networks over collaboration graphs to document analysis. Recent work has extended t…
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift
Stephan Rabanser, Stephan Günnemann, Zachary C. Lipton
We might hope that when faced with unexpected inputs, well-designed software systems would fire off warnings. Machine learning (ML) systems, however, which depend strongly on prope…
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…
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…
Adversarial Attacks on Neural Networks for Graph Data
Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness t…
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…