Search Results for author: Daniel Zügner

Found 22 papers, 15 papers with code

Adversarial Training for Graph Neural Networks: Pitfalls, Solutions, and New Directions

no code implementations NeurIPS 2023 Lukas Gosch, Simon Geisler, Daniel Sturm, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

Including these contributions, we demonstrate that adversarial training is a state-of-the-art defense against adversarial structure perturbations.

Graph Learning

On the Robustness and Anomaly Detection of Sparse Neural Networks

no code implementations9 Jul 2022 Morgane Ayle, Bertrand Charpentier, John Rachwan, Daniel Zügner, Simon Geisler, Stephan Günnemann

The robustness and anomaly detection capability of neural networks are crucial topics for their safe adoption in the real-world.

Anomaly Detection

Monte Carlo EM for Deep Time Series Anomaly Detection

1 code implementation29 Dec 2021 François-Xavier Aubet, Daniel Zügner, Jan Gasthaus

Identifying the anomalous points can be a goal on its own (anomaly detection), or a means to improving performance of other time series tasks (e. g. forecasting).

Anomaly Detection Time Series +1

Robustness of Graph Neural Networks at Scale

2 code implementations NeurIPS 2021 Simon Geisler, Tobias Schmidt, Hakan Şirin, Daniel Zügner, Aleksandar Bojchevski, Stephan Günnemann

Graph Neural Networks (GNNs) are increasingly important given their popularity and the diversity of applications.

A Study of Joint Graph Inference and Forecasting

no code implementations10 Sep 2021 Daniel Zügner, François-Xavier Aubet, Victor Garcia Satorras, Tim Januschowski, Stephan Günnemann, Jan Gasthaus

We study a recent class of models which uses graph neural networks (GNNs) to improve forecasting in multivariate time series.

Graph Learning Time Series +1

On Out-of-distribution Detection with Energy-based Models

1 code implementation3 Jul 2021 Sven Elflein, Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

Several density estimation methods have shown to fail to detect out-of-distribution (OOD) samples by assigning higher likelihoods to anomalous data.

Density Estimation Out-of-Distribution Detection +1

Reliable Graph Neural Networks via Robust Aggregation

1 code implementation NeurIPS 2020 Simon Geisler, Daniel Zügner, Stephan Günnemann

Perturbations targeting the graph structure have proven to be extremely effective in reducing the performance of Graph Neural Networks (GNNs), and traditional defenses such as adversarial training do not seem to be able to improve robustness.

Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts

1 code implementation NeurIPS 2020 Bertrand Charpentier, Daniel Zügner, Stephan Günnemann

The posterior distributions learned by PostNet accurately reflect uncertainty for in- and out-of-distribution data -- without requiring access to OOD data at training time.

Out of Distribution (OOD) Detection

Oktoberfest Food Dataset

1 code implementation22 Nov 2019 Alexander Ziller, Julius Hansjakob, Vitalii Rusinov, Daniel Zügner, Peter Vogel, Stephan Günnemann

We release a realistic, diverse, and challenging dataset for object detection on images.

Object object-detection +1

Certifiable Robustness and Robust Training for Graph Convolutional Networks

1 code implementation28 Jun 2019 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 outcomes unreliable.

Node Classification

Adversarial Attacks on Neural Networks for Graph Data

1 code implementation21 May 2018 Daniel Zügner, Amir Akbarnejad, Stephan Günnemann

Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given.

General Classification Node Classification

NetGAN: Generating Graphs via Random Walks

2 code implementations ICML 2018 Aleksandar Bojchevski, Oleksandr Shchur, Daniel Zügner, Stephan Günnemann

NetGAN is able to produce graphs that exhibit well-known network patterns without explicitly specifying them in the model definition.

Graph Generation Link Prediction

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