Graph kernels have become an established and widely-used technique for solving
classification tasks on graphs. This survey gives a comprehensive overview of techniques …

Graphs are data structures that effectively represent relational data in the real world. Graph
representation learning is a significant task since it could facilitate various downstream …

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 …

Neural networks are typically designed to deal with data in tensor forms. In this paper, we
propose a novel neural network architecture accepting graphs of arbitrary structure. Given a …

Temporal graph networks (TGNs) have gained prominence as models for embedding
dynamic interactions, but little is known about their theoretical underpinnings. We establish …

Abstract Graph Neural Networks (GNN) have been shown to work effectively for modeling
graph structured data to solve tasks such as node classification, link prediction and graph …

Numerous important problems can be framed as learning from graph data. We propose a
framework for learning convolutional neural networks for arbitrary graphs. These graphs …

One characteristic that sets humans apart from modern learning-based computer vision
algorithms is the ability to acquire knowledge about the world and use that knowledge to …

Our work combines aspects of three promising paradigms in machine learning, namely,
attention mechanism, energy-based models, and associative memory. Attention is the power …

Graph kernels have attracted a lot of attention during the last decade, and have evolved into
a rapidly developing branch of learning on structured data. During the past 20 years, the …