C
Christopher Morris
Researcher at École Polytechnique de Montréal
Publications - 42
Citations - 4272
Christopher Morris is an academic researcher from École Polytechnique de Montréal. The author has contributed to research in topics: Computer science & Graph kernel. The author has an hindex of 16, co-authored 31 publications receiving 2500 citations. Previous affiliations of Christopher Morris include Massachusetts Institute of Technology & Technical University of Dortmund.
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Hierarchical Graph Representation Learning with Differentiable Pooling
TL;DR: DiffPool is proposed, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion.
Proceedings Article
Hierarchical graph representation learning with differentiable pooling
TL;DR: DiffPool as discussed by the authors learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer.
Journal ArticleDOI
Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks
Christopher Morris,Martin Ritzert,Matthias Fey,William L. Hamilton,Jan Eric Lenssen,Gaurav Rattan,Martin Grohe +6 more
TL;DR: In this article, a generalization of GNNs, called k-dimensional GNN (k-GNNs), is proposed, which can take higher-order graph structures at multiple scales into account.
Posted Content
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks
Christopher Morris,Martin Ritzert,Matthias Fey,William L. Hamilton,Jan Eric Lenssen,Gaurav Rattan,Martin Grohe +6 more
TL;DR: In this article, a generalization of GNNs, called $k$-dimensional GNN, was proposed, which can take higher-order graph structures at multiple scales into account.
Posted Content
TUDataset: A collection of benchmark datasets for learning with graphs.
Christopher Morris,Nils M. Kriege,Franka Bause,Kristian Kersting,Petra Mutzel,Marion Neumann +5 more
TL;DR: The TUDataset for graph classification and regression is introduced, which consists of over 120 datasets of varying sizes from a wide range of applications and provides Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.