Assoz. Prof. Dipl.-Inf. Dr. Nils Morten Kriege
1090 Wien
Lehrveranstaltungen
Wintersemester 2026
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051080 LP Softwarepraktikum mit Bachelorarbeit
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052322 VU Graph Learning: Deep Learning for Graphs
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053021 LP Praktikum Informatik 1
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053031 LP Praktikum Informatik 2
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053049 SE Masterseminar
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053613 VU Introduction to Machine Learning
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500501 SE Doktoranden Forschungsseminar - Data and Knowledge
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960036 VU IT und Data Science Grundlagen
Sommersemester 2026
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051080 LP Softwarepraktikum mit Bachelorarbeit
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052813 VU Scientific Data Management
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053021 LP Praktikum Informatik 1
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053031 LP Praktikum Informatik 2
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053049 SE Masterseminar
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053631 LP Data Analysis Project
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500501 SE Doktoranden Forschungsseminar - Data and Knowledge
Wintersemester 2025
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051080 LP Softwarepraktikum mit Bachelorarbeit
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052311 VU Data Mining
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052322 VU Graph Learning
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053021 LP Praktikum Informatik 1
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053031 LP Praktikum Informatik 2
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053049 SE Masterseminar
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053613 VU Introduction to Machine Learning
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500501 SE Doktoranden Forschungsseminar - Data and Knowledge
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960028 VU IT und Data Science Grundlagen
Publikationen
Spectral Basis Learning for Expressive Graph Neural Networks in Link Prediction. / Azizi, Niloofar; Kriege, Nils M.; Harvey, Nicholas et al.
in: Proceedings of the ... National Conference on Artificial Intelligence, Band 40, Nr. 24, 14.03.2026, S. 19640-19648.
Veröffentlichungen: Beitrag in Fachzeitschrift › Meeting Abstract/Conference Paper › Peer Reviewed
Weisfeiler and Leman Go Gambling: Why Expressive Lottery Tickets Win. / Kummer, Lorenz; Moustafa, Samir; Ehrlich, Anatol et al.
Proceedings of the 42nd International Conference on Machine Learning. Band 267 PMLR, 2025. S. 31991-32010 (Proceedings of Machine Learning Research (PMLR), Band 267).
Veröffentlichungen: Beitrag in Buch › Beitrag in Konferenzband › Peer Reviewed
Visualization and Analysis of the Loss Landscape in Graph Neural Networks. / Moustafa, Samir (Korresp. Autor*in); Kummer, Lorenz; Fetzel, Simon et al.
Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions: 34th International Conference on Artificial Neural Networks, Kaunas, Lithuania, September 9–12, 2025, Proceedings, Part V. Springer Cham, 2025. S. 69-81 (Lecture Notes in Computer Science).
Veröffentlichungen: Beitrag in Buch › Beitrag in Konferenzband › Peer Reviewed
Efficient Mixed Precision Quantization in Graph Neural Networks. / Moustafa, Samir; Kriege, Nils M.; Gansterer, Wilfried (Korresp. Autor*in).
41st IEEE International Conference on Data Engineering. 2025. S. 4038-4052.
Veröffentlichungen: Beitrag in Buch › Beitrag in Konferenzband › Peer Reviewed
On the Relationship Between Robustness and Expressivity of Graph Neural Networks. / Kummer, Lorenz; Gansterer, Wilfried; Kriege, Nils Morten.
Proceedings of the 28th International Conference on Artificial Intelligence and Statistics. Band 258 PMLR, 2025. S. 1243-1251 (Proceedings of Machine Learning Research (PMLR)).
Veröffentlichungen: Beitrag in Buch › Beitrag in Konferenzband › Peer Reviewed
Crossfire: An Elastic Defense Framework for Graph Neural Networks Under Bit Flip Attacks. / Kummer, Lorenz; Moustafa, Samir; Gansterer, Wilfried et al.
Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence. AAAI, 2025. S. 17990-17998 (Proceedings of the ... National Conference on Artificial Intelligence; Nr. 17, Band 39).
Veröffentlichungen: Beitrag in Buch › Beitrag in Konferenzband › Peer Reviewed
Maximally Expressive GNNs for Outerplanar Graphs. / Bause, Franka; Jogl, Fabian; Indri, Patrick et al.
in: Transactions on Machine Learning Research (TMLR), 03.01.2025.
Veröffentlichungen: Beitrag in Fachzeitschrift › Artikel › Peer Reviewed
Chemical Similarity and Substructure Searches. / Kriege, Nils Morten; Seidel, Thomas; Humbeck, Lina et al.
Encyclopedia of Bioinformatics and Computational Biology. Band 3 2. Aufl. Elsevier, 2025. S. 707-719.
Veröffentlichungen: Beitrag in Buch › Beitrag in Buch/Sammelband › Peer Reviewed
sc-GRIP: a Graph Convolutional Approach to Infer Gene Interaction Polarity from Single-cell Data. / Atria, Carolina Elisabeth; Cai, Yitao; Weber, Pascal et al.
2025. Beitrag in International Conference on Data Mining, Washington DC, USA / Vereinigte Staaten.
Veröffentlichungen: Beitrag zu Konferenz › Paper › Peer Reviewed
Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Leman Go Indifferent. / Kummer, Lorenz; Moustafa, Samir; Schrittwieser, Sebastian et al.
Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: KDD 2024. New York: Association for Computing Machinery (ACM), 2024. S. 1428-1439.
Veröffentlichungen: Beitrag in Buch › Beitrag in Konferenzband › Peer Reviewed
Projekte
Structured Data Learning with General Similarities
Geometric deep leaning based recommender engine with implicit feedback data in e-commerce domain (GNN Recsys)
Algorithmic Data Science for Computational Drug Discovery
Vorträge
Sc-GRIP: a Graph Convolutional Approach to Infer Gene Interaction Polarity from Single-Cell Data
Expressivity in Graph Learning: From Sets to Graphs
Activity Cliffs Go Smooth: Graph Siamese Neural Networks for Molecular Activity Prediction
Expressivity in Graph Learning: On Walks and the Weisfeiler-Leman Algorithm
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited
TUDataset: A collection of benchmark datasets for learning with graphs
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