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Christopher Morris
Room: | 238 |
Phone: | +49 231 755-7707 |
Fax: | +49 231 755-7740 |
E-Mail: | christopher.morristu-dortmund.de |
GitHub: | https://github.com/chrsmrrs |
Research Interests
- Machine learning with graphs,
- Algorithm Engineering, Algorithmic Data Analysis
- Combinatorial optimization and graph algorithms
Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis
Resource-efficient Graph Mining
Fun Fact: My Erdős number is at most 3 (via Petra Mutzel → Bojan Mohar → P. Erdős)
Publications
Refereed Conference Articles
- Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (Preprint
arXiv:1810.02244
)
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, Martin Grohe,
AAAI Conference on Artificial Intelligence (AAAI) 2019.
[Source Code][Slides]
- Hierarchical Graph Representation Learning with Differentiable Pooling (Preprint
arXiv:1806.08804
)
Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L. Hamilton, Jure Leskovec,
Neural Information Processing Systems (NeurIPS) 2019, spotlight presentation, and KDD Deep Learning Day 2018.
[Source Code]
- A Property Testing Framework for the Theoretical Expressivity of Graph Kernels
Nils M. Kriege, Christopher Morris, Anja Rey, Christian Sohler,
International Joint Conference on Artificial Intelligence (IJCAI) 2018.
- Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs (Preprint
arXiv:1703.02379
)
Christopher Morris, Kristian Kersting, Petra Mutzel,
IEEE International Conference on Data Mining (IEEE ICDM) 2017, full paper.
[Source Code] [Slides]
- Recent Advances in Kernel-Based Graph Classification
Nils M. Kriege, Christopher Morris,
European Conference on Machine Learning & Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) 2017, nectar track.
[Slides]
- Faster Kernels for Graphs with Continuous Attributes via Hashing (Preprint
arXiv:1610.00064
)
Christopher Morris, Nils M. Kriege, Kristian Kersting, Petra Mutzel,
IEEE International Conference on Data Mining (IEEE ICDM) 2016.
[Source Code]
Refereed Journal Articles
- Output-sensitive Complexity of Multiobjective Combinatorial Optimization (Preprint
arXiv:1610.07204
)
Fritz Bökler, Matthias Ehrgott, Christopher Morris, Petra Mutzel,
Journal of Multicriteria Decision Analysis, 2016.
Preprints and Working Papers
- A Unifying View of Explicit and Implicit Feature Maps for Structured Data: Systematic Studies of Graph Kernels (Preprint
arXiv:1703.00676
)
Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel.
Teaching
Supervised Students
- Marcel Walker (BT, 2016): Dimensionsreduktion von Merkmalsvektoren für explizite Graphkerne
- Christopher Osthues (BT, 2016): Experimenteller Vergleich von Labeling-Verfahren für Graphkerne
- Serdar Ayaz (BT, 2016): Approximation des Weisfeiler-Lehman-Isomorphie-Tests durch Sampling
- Franka Bause (BT, 2017): Approximation der Editierdistanz für Graphen in linearer Zeit
- Ryan Ladwig (Student intern from NAU, 2017): Design and Implementation of k-disk Graph Kernels
- Jannis Junge (MT, 2018): Approximation des Graphenspektrums
- Marcel Walker (MT, 2018): Systematisierung von neuronalen Netzen auf Graphen
- Nina Runde (BT, 2018): Evaluation von Varianten des $k$-dimensionalen Weisfeiler-Leman Algorithmus
Sommersemester 2018
Wintersemester 2017/18
Sommersemester 2017
Wintersemester 2016/17
Sommersemester 2016
Wintersemester 2015/16
Benchmark Data Sets
Bibliography of (Supervised) Graph Classification
Activities
- 27.01.2019–01.02.2019: AAAI Conference on Artificial Intelligence (Poster)
- 02.12.2018–08.12.2018: Conference on Neural Information Processing Systems 2018 (Spotlight Tallk + Poster)
- 15.01.2018–31.03.2018: Research stay at Stanford University with Jure Leskovec
- 18.11.2017–21.11.2017: IEEE International Conference on Data Mining 2017 (Talk)
- 18.09.2017–22.09.2017: ECML PKKD 2017 (Talk)
- 19.06.2017–30.06.2017: The Machine Learning Summer School (Poster)
- 09.06.2017–11.06.2017: Highlights of Algorithms 2017
- 12.12.2016–15.12.2016: IEEE International Conference on Data Mining 2016 (Talk)
- 22.02.–26.02.2016: Indo-German Spring School on Algorithms for Big Data
- 28.09.–30.09.2015: ACBD 2015 -- Algorithmic Challenges of Big Data