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Oktoberfest Food Dataset
Alexander Ziller, Julius Hansjakob, Vitalii Rusinov +3
We release a realistic, diverse, and challenging dataset for object detection on images. The data was recorded at a beer tent in Germany and consists of 15 different categories of…
Uncertainty on Asynchronous Time Event Prediction
Marin Biloš, Bertrand Charpentier, Stephan Günnemann
Asynchronous event sequences are the basis of many applications throughout different industries. In this work, we tackle the task of predicting the next event (given a history), an…
Diffusion Improves Graph Learning
Johannes Gasteiger, Stefan Weißenberger, Stephan Günnemann
Graph convolution is the core of most Graph Neural Networks (GNNs) and usually approximated by message passing between direct (one-hop) neighbors. In this work, we remove the restr…
Group Centrality Maximization for Large-scale Graphs
Eugenio Angriman, Alexander van der Grinten, Aleksandar Bojchevski +3
The study of vertex centrality measures is a key aspect of network analysis. Naturally, such centrality measures have been generalized to groups of vertices; for popular measures i…
Certifiable Robustness to Graph Perturbations
Aleksandar Bojchevski, Stephan Günnemann
Despite the exploding interest in graph neural networks there has been little effort to verify and improve their robustness. This is even more alarming given recent findings showin…
Overlapping Community Detection with Graph Neural Networks
Oleksandr Shchur, Stephan Günnemann
Community detection is a fundamental problem in machine learning. While deep learning has shown great promise in many graphrelated tasks, developing neural models for community det…
Intensity-Free Learning of Temporal Point Processes
Oleksandr Shchur, Marin Biloš, Stephan Günnemann
Temporal point processes are the dominant paradigm for modeling sequences of events happening at irregular intervals. The standard way of learning in such models is by estimating t…
Certifiable Robustness and Robust Training for Graph Convolutional Networks
Daniel Zügner, Stephan Günnemann
Recent works show that Graph Neural Networks (GNNs) are highly non-robust with respect to adversarial attacks on both the graph structure and the node attributes, making their outc…
GhostLink: Latent Network Inference for Influence-aware Recommendation
Subhabrata Mukherjee, Stephan Guennemann
Social influence plays a vital role in shaping a user's behavior in online communities dealing with items of fine taste like movies, food, and beer. For online recommendation, this…
Adversarial Attacks on Graph Neural Networks via Meta Learning
Daniel Zügner, Stephan Günnemann
Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time…