Daniel Zügner

Artificial Intelligence · 2018–2025

A researcher in the Science corpus: 7 papers; trusts 132 researchers and is trusted by 37 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 116 citations into it; #4,488 of 35,345 by trust here, #8,007 by citations
• Graph Learning — 53 citations into it; #211 of 693 by trust here, #266 by citations
• Statistical and Nonlinear Physics — 34 citations into it
• Computer Vision and Pattern Recognition — 32 citations into it; #15,174 of 21,575 by trust here, #12,689 by citations
• Representation & Self-Supervised Learning — 29 citations into it
• Information Systems — 26 citations into it
• Generative Models — 22 citations into it; #1,443 of 1,956 by trust here, #1,382 by citations
• Safety, Robustness & Interpretability — 19 citations into it; #931 of 2,251 by trust here, #1,265 by citations

Whom Daniel Zügner trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Yoshua Bengio
2. Geoffrey E. Hinton
3. Diederik P. Kingma
4. Aleksandar Bojchevski
5. Alex Krizhevsky
6. Ilya Sutskever
7. Max Welling
8. Yann LeCun
9. Thomas Kipf
10. Stephan Günnemann

Who trusts Daniel Zügner overall

1. Marinka Zitnik
2. Aleksandar Bojchevski
3. Stephan Günnemann
4. Lichao Sun
5. Wei Jin
6. Binghui Wang
7. Kaidi Xu
8. Yingtong Dou
9. Yao Ma
10. Kaiyi Ji

Papers from Daniel Zügner's perspective

Ordered by how strongly the researchers Daniel Zügner trusts endorse each paper. Same corpus, a different order for every researcher.

1. ImageNet classification with deep convolutional neural networks
2. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
3. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
4. Attention is All you Need (Neural Information Processing Systems)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. Learning Multiple Layers of Features from Tiny Images
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)

Their papers

• NetGAN: Generating Graphs via Random Walks (2018) — 410 citations
• Adversarial Attacks on Neural Networks for Graph Data (2018) — 1,271 citations
• A generative model for inorganic materials design (2025) — 567 citations
• MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures (2024) — 240 citations
• Language-Agnostic Representation Learning of Source Code from Structure and Context (2021) — 133 citations
• Certifiable Robustness and Robust Training for Graph Convolutional Networks (2019) — 180 citations
• Adversarial Attacks on Graph Neural Networks via Meta Learning (2019) — 659 citations