Graph Neural Networks (GNNs) have exploded onto the machine learning scene in recent
years owing to their capability to model and learn from graph-structured data. Such an ability …

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

Abstract We introduce Graph of Thoughts (GoT): a framework that advances prompting
capabilities in large language models (LLMs) beyond those offered by paradigms such as …

While Graph Neural Networks (GNNs) have achieved remarkable results in a variety of
applications, recent studies exposed important shortcomings in their ability to capture the …

This paper addresses the challenging problem of retrieval and matching of graph structured
objects, and makes two key contributions. First, we demonstrate how Graph Neural …

In this paper, we propose a flexible notion of characteristic functions defined on graph
vertices to describe the distribution of vertex features at multiple scales. We introduce …

Graph similarity search is among the most important graph-based applications, eg finding
the chemical compounds that are most similar to a query compound. Graph …

In this paper, we present Deep Graph Kernels, a unified framework to learn latent
representations of sub-structures for graphs, inspired by latest advancements in language …

Vector representations of graphs and relational structures, whether hand-crafted feature
vectors or learned representations, enable us to apply standard data analysis and machine …

In this article, we propose a family of efficient kernels for large graphs with discrete node
labels. Key to our method is a rapid feature extraction scheme based on the Weisfeiler …