Graph-based machine learning is experiencing explosive growth, driven by impressive
recent developments and wide applicability. Typical approaches for graph representation …

Due to their ability to model meaningful higher order relations among a set of entities, higher
order network models have emerged recently as a powerful alternative for graph-based …

Network data has become widespread, larger, and more complex over the years. Traditional
network data is dyadic, capturing the relations among pairs of entities. With the need to …

The pairwise interaction paradigm of graph machine learning has predominantly governed
the modelling of relational systems. However, graphs alone cannot capture the multi-level …

Networks—or graphs—are universal descriptors of systems of interacting elements. In
biomedicine and healthcare, they can represent, for example, molecular interactions …

Graph representation learning methods have mostly been limited to the modelling of node-
wise interactions. Recently, there has been an increased interest in understanding how …

We present simplicial neural networks (SNNs), a generalization of graph neural networks to
data that live on a class of topological spaces called simplicial complexes. These are natural …

In this paper, we introduce $\texttt {TopoSRL} $, a novel self-supervised learning (SSL)
method for simplicial complexes to effectively capture higher-order interactions and preserve …

Graph representation learning for hypergraphs can be used to extract patterns among
higher-order interactions that are critically important in many real world problems. Current …

Graph-structured data, where nodes exhibit either pair-wise or high-order relations, are
ubiquitous and essential in graph learning. Despite the great achievement made by existing …