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.
Papers
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Journal ArticleDOI
A Survey on Graph Kernels
TL;DR: Graph kernels have become an established and widely used technique for solving classification tasks on graphs as mentioned in this paper, and a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years is given in this survey.
Journal ArticleDOI
A survey on graph kernels
TL;DR: This survey gives a comprehensive overview of techniques for kernel-based graph classification developed in the past 15 years and describes and categorizes graph kernels based on properties inherent to their design, such as the nature of their extracted graph features, their method of computation and their applicability to problems in practice.
Proceedings Article
Deep Graph Matching Consensus
TL;DR: This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs that scales well to large, real-world inputs while still being able to recover global correspondences consistently.
Proceedings ArticleDOI
Glocalized Weisfeiler-Lehman Graph Kernels: Global-Local Feature Maps of Graphs
TL;DR: This work introduces a novel graph kernel based on the k-dimensional Weisfeiler-Lehman algorithm, and devise a stochastic version of the kernel with provable approximation guarantees using conditional Rademacher averages.
Proceedings ArticleDOI
Faster Kernels for Graphs with Continuous Attributes via Hashing
TL;DR: Hash graph kernels as discussed by the authors is a general framework to derive kernels for graphs with continuous attributes from discrete ones by iteratively turning continuous attributes into discrete labels using randomized hash functions, which is shown to be scalable to large graphs and data sets.