Network-based modeling of complex systems and data using the language of graphs has
become an essential topic across a range of different disciplines. Arguably, this graph-based …

Shape is data and data is shape. Biologists are accustomed to thinking about how the shape
of biomolecules, cells, tissues, and organisms arise from the effects of genetics …

Selecting a learning algorithm to implement for a particular application on the basis of
performance still remains an ad-hoc process using fundamental benchmarks such as …

Graph neural networks (GNNs) have emerged as a powerful tool for graph classification and
representation learning. However, GNNs tend to suffer from over-smoothing problems and …

Abstract The Weisfeiler–Lehman graph kernel exhibits competitive performance in many
graph classification tasks. However, its subtree features are not able to capture connected …

In this paper we develop an alternative topological data analysis (TDA) approach for
studying graph representations of time series of dynamical systems. Specifically, we show …

Datasets are mathematical objects (eg, point clouds, matrices, graphs, images,
fields/functions) that have shape. This shape encodes important knowledge about the …

Chatter identification and detection in machining processes has been an active area of
research in the past two decades. Part of the challenge in studying chatter is that machining …

This work presents a framework for studying temporal networks using zigzag persistence, a
tool from the field of Topological Data Analysis (TDA). The resulting approach is general and …

In this work we study several learning strategies for fluid sloshing problems based on data.
In essence, a reduced-order model of the dynamics of the free surface motion of the fluid is …