Codes and Expansions (CodEx) Seminar

Ishika Ghosh (Michigan State University):
Towards an Optimal Bound for the Interleaving Distance on Mapper Graphs

Mapper graphs are a widely used tool in topological data analysis and visualization. They can be viewed as discrete approximations of Reeb graphs, offering insight into the shape and connectivity of complex data. Given a high-dimensional point cloud \(\mathbb{X}\) equipped with a function \(f: \mathbb{X} \to \mathbb{R}\), a mapper graph provides a summary of the topological structure of \(\mathbb{X}\) induced by \(f\), where each node represents a local neighborhood, and edges connect nodes whose corresponding neighborhoods overlap. Our focus is the interleaving distance for mapper graphs, arising from a discretization of the version for Reeb graphs, which is NP-hard to compute. This distance quantifies the similarity between two mapper graphs by measuring the extent to which they must be "stretched" to become comparable.

In this talk, I’ll introduce a new, computationally feasible approach to estimating interleaving distance via a loss function that upper-bounds it. We employ a categorical formulation of mapper graphs and develop the first framework for computing the associated loss function. Since the quality of the bound depends on the chosen assignment, we optimize this loss function by formulating the problem of finding the best assignment as an integer linear programming problem. To evaluate the effectiveness of our optimization, we apply it to small mapper graphs where the interleaving distance is known, demonstrating that the optimized upper bound successfully matches the interleaving distance in these cases. Additionally, we experiment on small benchmarking datasets, computing the pairwise optimal loss on a collection of mapper graphs derived from the data and leveraging the distance bound for image classification.