Rohit Roy

h-index7
2papers
298citations

2 Papers

4.7CGJul 1
The Singular Source of Vineyard Monodromy

Erin W. Chambers, Christopher Fillmore, Shankha Shubhra Mukherjee et al.

Vineyards, or time-varying families of persistence diagrams, are widely used in topological data analysis (TDA) pipelines to track how topological features change and evolve as a parameter varies. When the parameter traces a closed loop, a vineyard can exhibit monodromy: diagram points permute over the course of a full traversal, which obstructs feature tracking and can complicate downstream analysis of such data. Chambers et al. considered the periodic vineyards that arise from the radial persistence transform, which maps the manifold to a family of persistence diagrams, where each diagram fixes a base point and considers the filtration that is based on Euclidean distance to that point, and showed that monodromy and knotting can occur. Other recent work by Arya et al. considers geometric conditions that exclude monodromy in two dimensions, in an effort to better understand when this effect happens. That said, understanding when and why monodromy occurs is a fundamental open problem with direct practical consequences for many data analysis pipelines. In this work, we study this question for 1-manifolds in $\mathbb{R}^2$, using a surprising connection with tools from singularity theory, and provide a classification for the causes of monodromy in vineyards. More precisely, we prove that the vineyard of a sufficiently small loop $γ$ cannot exhibit monodromy unless it contains a specific singularity of the distance function. The central geometric object in our analysis is the symmetry set, which is the locus of centers of spheres tangent in more than one point to the manifold; this object classifies singularities of the distance function, and in our setting, dictates precisely when monodromy occurs. This characterization opens the door to the development of algorithmic criteria for detecting and utilizing (or avoiding) monodromy in TDA pipelines.

4.1LGFeb 14, 2025Code
Classification of Temporal Graphs using Persistent Homology

Siddharth Pritam, Rohit Roy, Madhav Cherupilil Sajeev

Temporal graphs effectively model dynamic systems by representing interactions as timestamped edges. However, analytical tools for temporal graphs are limited compared to static graphs. We propose a novel method for analyzing temporal graphs using Persistent Homology. Our approach leverages $δ$-temporal motifs (recurrent subgraphs) to capture temporal dynamics %without aggregation . By evolving these motifs, we define the \textit{average filtration} and compute PH on the associated clique complex. This method captures both local and global temporal structures and is stable with respect to reference models. We demonstrate the applicability of our approach to the temporal graph classification task. Experiments verify the effectiveness of our approach, achieving over 92\% accuracy, with some cases reaching 100\%. Unlike existing methods that require node classes, our approach is node class free, offering flexibility for a wide range of temporal graph analysis.