Topological deep learning (TDL) is a rapidly evolving field that uses topological features to
understand and design deep learning models. This paper posits that TDL may complement …

Melanoma response to immune-modulating therapy remains incompletely characterized at
the molecular level. In this study, we assess melanoma immunotherapy response using a …

Persistent homology, a technique from computational topology, has recently shown strong
empirical performance in the context of graph classification. Being able to capture long …

Persistent homology, a technique from computational topology, has recently shown strong
empirical performance in the context of graph classification. Being able to capture long …

Causal inference on networks faces challenges posed in part by violations of standard
identification assumptions due to dependencies between treatment units. Although graph …

Recently, there has been an emerging trend to integrate persistent homology (PH) into
graph neural networks (GNNs) to enrich expressive power. However, naively plugging PH …

Among the various generalizations of persistent topology, that based on rank functions and
leading to indexing-aware functions appears to be particularly suited to catching graph …

Learning causal mechanisms involving networked units of data is a notoriously challenging
task with various applications. Graph Neural Networks (GNNs) have proven to be effective …

Graph neural networks have become the default choice by practitioners for graph learning
tasks such as graph classification and node classification. Nevertheless, popular graph …

Identifying (a) systemic barriers to quality healthcare access and (b) key indicators of care
efficacy in the United States remains a significant challenge. To improve our understanding …