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 …

This work considers the problem of computing distances between structured objects such as
undirected graphs, seen as probability distributions in a specific metric space. We consider a …

This work considers the problem of computing distances between structured objects such as
undirected graphs, seen as probability distributions in a specific metric space. We consider a …

Dictionary learning is a key tool for representation learning, that explains the data as linear
combination of few basic elements. Yet, this analysis is not amenable in the context of graph …

Optimal transport theory has recently found many applications in machine learning thanks to
its capacity to meaningfully compare various machine learning objects that are viewed as …

Semi-supervised node classification on graphs is an important research problem, with many
real-world applications in information retrieval such as content classification on a social …

Cuneiform tablets appertain to the oldest textual artifacts used for more than three millennia
and are comparable in amount and relevance to texts written in Latin or ancient Greek …

Despite recent progress in Graph Neural Networks (GNNs), explaining predictions made by
GNNs remains a challenging and nascent problem. The leading method mainly considers …

As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown
the potential for matching problems of structured data like point clouds and graphs …

In recent years there has been a rapid increase in classification methods on graph
structured data. Both in graph kernels and graph neural networks, one of the implicit …