My research combines techniques from machine learning, graph algorithms, and CS theory, and revolves around the following questions:

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

Publications (Scholar)

Conference Papers

• Weisfeiler and Leman Go Relational
  Pablo Barcelo, Mikhail Galkin, Christopher Morris, Miguel Romero Orth,
  
  Learning on Graphs (LoG) 2022.

• Ordered Subgraph Aggregation Networks (Preprint arxiv:2206.11168)
  Chendi Qian, Gaurav Rattan, Floris Geerts, Christopher Morris, Mathias Niepert,
  
  Neural Information Processing Systems (NeurIPS) 2022.

• SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks (Preprint arXiv:2203.13913)
  Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh,
  International Conference on Machine Learning (ICML) 2022.

• MIP-GNN: A data-driven framework for guiding combinatorial solvers (Preprint arXiv:2205.14210)
  Elias B. Khalil, Christopher Morris, Andrea Lodi,
  AAAI Conference on Artificial Intelligence (AAAI) 2022.

• Reconstruction for Powerful Graph Representations (Preprint arXiv:2110.00577)
  Leonardo Cotta, Christopher Morris, Bruno Ribeiro,
  Neural Information Processing Systems (NeurIPS) 2021.

• Combinatorial optimization and reasoning with graph neural networks (Preprint arXiv:2102.09544)
  Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic,
  International Joint Conference on Artificial Intelligence (IJCAI) 2021.

• The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs (Preprint arXiv:2105.05911)
  Christopher Morris, Matthias Fey, Nils M. Kriege,
  International Joint Conference on Artificial Intelligence (IJCAI) 2021.

• Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (Preprint arXiv:1904:01543)
  Christopher Morris, Gaurav Rattan, Petra Mutzel,
  Neural Information Processing Systems (NeurIPS) 2020.

• Deep Graph Matching Consensus (Preprint arXiv:2001:09621)
  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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

Journal Papers

• Classifying Dissemination Processes in Temporal Graphs
  Lutz Oettershagen, Nils M. Kriege, Christopher Morris, Petra Mutzel,
  Big Data, 2020.

• A Survey on Graph Kernels (Preprint arXiv:1903.11835)
  Nils M. Kriege, Fredrik D. Johansson, Christopher Morris,
  Applied Network Science, Machine learning with graphs, 2020.

• 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.

• 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.

Book Chapters

• The Weisfeiler-Leman Method for Machine Learning with Graphs
  Nils M. Kriege, Christopher Morris,
  Graph Neural Networks: Foundations, Frontiers, and Applications, De Gruyter, 2022.

• Graph Neural Networks: Graph Classification
  Christopher Morris,
  Graph Neural Networks: Foundations, Frontiers, and Applications, Springer, 2022.

Workshop Papers

• SpeqNets: Sparsity-aware Permutation-equivariant Graph Networks (Preprint arXiv:2203.13913)
  Christopher Morris, Gaurav Rattan, Sandra Kiefer, Siamak Ravanbakhsh,
  Geometrical and Topological Representation Learning (GT-RL, ICLR 2022).

• Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings (Preprint arXiv:1904.01543)
  Christopher Morris, Gaurav Rattan, Petra Mutzel,
  Graph Representation Learning and Beyond (GRL+, ICML 2020).

• TUDataset: A collection of benchmark datasets for learning with graphs (Preprint arXiv:2007.08663)
  Christopher Morris, Nils M. Kriege, Franka Bause Kristian Kersting, Petra Mutzel, Marion Neumann,
  Graph Representation Learning and Beyond (GRL+, ICML 2020).

Edited Workshop and Competition Proceedings

• The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights (Preprint 2203.02433)
  Maxime Gasse, Quentin Cappart, Jonas Charfreitag, Laurent Charlin, Didier Chételat, Antonia Chmiela, Justin Dumouchelle, Ambros Gleixner, Aleksandr M. Kazachkov, Elias Khalil, Pawel Lichocki, Andrea Lodi, Miles Lubin, Chris J. Maddison, Christopher Morris, Dimitri J. Papageorgiou, Augustin Parjadis, Sebastian Pokutta, Antoine Prouvost, Lara Scavuzzo, Giulia Zarpellon, Linxin Yang, Sha Lai, Akang Wang, Xiaodong Luo, Xiang Zhou, Haohan Huang, Shengcheng Shao, Yuanming Zhu, Dong Zhang, Tao Quan, Zixuan Cao, Yang Xu, Zhewei Huang, Shuchang Zhou, Chen Binbin, He Minggui, Hao Hao, Zhang Zhiyu, An Zhiwu, Mao Kun,
  NeurIPS 2021 Competitions and Demonstrations Track, Proceedings of Machine Learning Research.

• Graph Embeddings: Theory meets Practice (Dagstuhl Seminar 22132)
  Martin Grohe, Stephan Günnemann, Stefanie Jegelka, Christopher Morris,
  Dagstuhl Reports 12 (3), 2022.

Working Papers

• WL meet VC (Preprint arXiv:2301.11039)
  Christopher Morris, Floris Geerts, Jan Tönshoff, Martin Grohe.
  

• Weisfeiler and Leman go Maching Learning: The Story so far (Preprint arXiv:2112.09992)
  Christopher Morris, Yaron Lipman, Haggai Maron, Bastian Rieck, Nils M. Kriege, Martin Grohe, Matthias Fey, Karsten Borgwardt.
  

• Combinatorial optimization and reasoning with graph neural networks (Preprint arXiv:2102.09544)
  Quentin Cappart, Didier Chételat, Elias B. Khalil, Andrea Lodi, Christopher Morris, Petar Velickovic.
  

Thesis

• Learning with graphs: kernel and neural approaches
  Christopher Morris.
  

Teaching