Christopher Morris

Contact

christopher.morris@tu-dortmund.de
Otto-Hahn-Straße 14
@chrsmrrs
chrsmrrs

I am a PhD student at TU Dortmund University advised by Petra Mutzel. I also frequently collaborated with Kristian Kersting. My PhD research is generously supported by the German Research Foundation, through the Collaborative Research Center SFB 876 -- Providing Information by Resource-Constrained Data Analysis, Project A6.

I develop machine learning methods for graphs, network, and relational data.
My research combines techniques from machine learning, graph algorithms, and CS theory, and revolves around the following questions:

How do we effectively capture the structure of graphs and networks in a data-driven manner?
How can we scale up such methods for large-scale data?
How can such methods make combinatorial algorithms faster in a data-driven manner?

For more details, please refer my CV.

Fun Fact: My Erdős number is at most 3 (via Petra Mutzel → Bojan Mohar → P. Erdős)

Conference Papers

Deep Graph Matching Consensus
Matthias Fey, Jan E. Lenssen, Christopher Morris, Jonathan Masci, Nils M. Kriege.
International Conference on Learning Representations (ICLR) 2020.

Temporal Graph Kernels for Classifying Dissemination Processes (Preprint arXiv:1911.05496)
Lutz Oettershagen, Nils Kriege, Christopher Morris, Petra Mutzel.
SIAM International Conference on Data Mining (SDM) 2020.

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) 2018, 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

Journal Papers

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.

A Survey on Graph Kernels (Preprint arXiv:1903.11835)
Nils M. Kriege, Fredrik D. Johansson, Christopher Morris,
Applied Network Science, Machine learning with graphs, 2019, accepted for publication.

A unifying view of explicit and implicit feature maps of graph kernels (Preprint arXiv:1703.00676)
Nils M. Kriege, Marion Neumann, Christopher Morris, Kristian Kersting, Petra Mutzel,
Data Mining and Knowledge Discovery, 2019, accepted for publication.

Preprints and Working Papers

Towards a practical $k$-dimensional Weisfeiler-Leman algorithm (Preprint arXiv:1904.01543)
Christopher Morris, Petra Mutzel.

27.01.2019–01.02.2019: AAAI Conference on Artificial Intelligence (Spotlight Talk + Poster)

02.12.2018–08.12.2018: Conference on Neural Information Processing Systems 2018 (Spotlight Talk + 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

I maintain a growing collection of benchmark datasets for supervised graph classification.