389 citations · 1.1k across the 26 of their papers we have counts for
14 papers · 1 filter
On the Two Sides of Redundancy in Graph Neural Networks
Franka Bause, Samir Moustafa, Johannes Langguth +2
Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes. With increasing depth, information from more distant nodes i…
Gradual Weisfeiler-Leman: Slow and Steady Wins the Race
Franka Bause, Nils M. Kriege
The classical Weisfeiler-Leman algorithm aka color refinement is fundamental for graph learning with kernels and neural networks. Originally developed for graph isomorphism testing…
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited
Nils M. Kriege
Random walk kernels have been introduced in seminal work on graph learning and were later largely superseded by kernels based on the Weisfeiler-Leman test for graph isomorphism. We…
Weisfeiler and Leman go Machine Learning: The Story so far
Christopher Morris, Yaron Lipman, Haggai Maron +5
In recent years, algorithms and neural architectures based on the Weisfeiler--Leman algorithm, a well-known heuristic for the graph isomorphism problem, have emerged as a powerful…
The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs
Christopher Morris, Matthias Fey, Nils M. Kriege
In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool f…
TUDataset: A collection of benchmark datasets for learning with graphs
Christopher Morris, Nils M. Kriege, Franka Bause +3
Recently, there has been an increasing interest in (supervised) learning with graph data, especially using graph neural networks. However, the development of meaningful benchmark d…
Deep Graph Matching Consensus
Matthias Fey, Jan E. Lenssen, Christopher Morris +2
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs. First, we use localized node embeddings computed by a graph…
Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning
Nils M. Kriege
Kernels for structured data are commonly obtained by decomposing objects into their parts and adding up the similarities between all pairs of parts measured by a base kernel. Assig…
A Survey on Graph Kernels
Nils M. Kriege, Fredrik D. Johansson, Christopher Morris
Graph kernels have become an established and widely-used technique for solving classification tasks on graphs. This survey gives a comprehensive overview of techniques for kernel-b…
Computing Optimal Assignments in Linear Time for Approximate Graph Matching
Nils M. Kriege, Pierre-Louis Giscard, Franka Bause +1
Finding an optimal assignment between two sets of objects is a fundamental problem arising in many applications, including the matching of `bag-of-words' representations in natural…
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels
Nils M. Kriege, Marion Neumann, Christopher Morris +2
Non-linear kernel methods can be approximated by fast linear ones using suitable explicit feature maps allowing their application to large scale problems. We investigate how convol…
Faster Kernels for Graphs with Continuous Attributes via Hashing
Christopher Morris, Nils M. Kriege, Kristian Kersting +1
While state-of-the-art kernels for graphs with discrete labels scale well to graphs with thousands of nodes, the few existing kernels for graphs with continuous attributes, unfortu…
On Valid Optimal Assignment Kernels and Applications to Graph Classification
Nils M. Kriege, Pierre-Louis Giscard, Richard C. Wilson
The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data. A leading design paradigm for this is the convol…
Subgraph Matching Kernels for Attributed Graphs
Nils Kriege, Petra Mutzel
We propose graph kernels based on subgraph matchings, i.e. structure-preserving bijections between subgraphs. While recently proposed kernels based on common subgraphs (Wale et al.…