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Found 17 papers, 6 papers with code
On the Two Sides of Redundancy in Graph Neural Networks
Message passing neural networks iteratively generate node embeddings by aggregating information from neighboring nodes.
Gradual Weisfeiler-Leman: Slow and Steady Wins the Race
The classical Weisfeiler-Leman algorithm aka color refinement is fundamental for graph learning with kernels and neural networks.
A Temporal Graphlet Kernel for Classifying Dissemination in Evolving Networks
We introduce the \emph{temporal graphlet kernel} for classifying dissemination processes in labeled temporal graphs.
Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited
Random walk kernels have been introduced in seminal work on graph learning and were later largely superseded by kernels based on the Weisfeiler-Leman test for graph isomorphism.
Temporal Walk Centrality: Ranking Nodes in Evolving Networks
We propose the Temporal Walk Centrality, which quantifies the importance of a node by measuring its ability to obtain and distribute information in a temporal network.
Weisfeiler and Leman go Machine Learning: The Story so far
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 tool for machine learning with graphs and relational data.
The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs
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 for (supervised) machine learning with graphs and relational data.
TUDataset: A collection of benchmark datasets for learning with graphs
We provide Python-based data loaders, kernel and graph neural network baseline implementations, and evaluation tools.
Deep Graph Matching Consensus
This work presents a two-stage neural architecture for learning and refining structural correspondences between graphs.
Ranked #12 on
Entity Alignment
on DBP15k zh-en
(using extra training data)
Temporal Graph Kernels for Classifying Dissemination Processes
Hence, we confirm that taking temporal information into account is crucial for the successful classification of dissemination processes.
Deep Weisfeiler-Lehman Assignment Kernels via Multiple Kernel Learning
Assignment kernels are based on an optimal bijection between the parts and have proven to be an effective alternative to the established convolution kernels.
A Survey on Graph Kernels
Graph kernels have become an established and widely-used technique for solving classification tasks on graphs.
Computing Optimal Assignments in Linear Time for Approximate Graph Matching
In this paper, we develop an algorithm which can find an optimal assignment in linear time when the cost function between objects is represented by a tree distance.
Recognizing Cuneiform Signs Using Graph Based Methods
To this end, the distance measure is used to implement a nearest neighbor classifier leading to a high computational cost for the prediction phase with increasing training set size.
A Unifying View of Explicit and Implicit Feature Maps of Graph Kernels
On this basis we propose exact and approximative feature maps for widely used graph kernels based on the kernel trick.
Faster Kernels for Graphs with Continuous Attributes via Hashing
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, unfortunately, do not scale well.
On Valid Optimal Assignment Kernels and Applications to Graph Classification
The success of kernel methods has initiated the design of novel positive semidefinite functions, in particular for structured data.
Ranked #2 on
Graph Classification
on NCI109
