Graph Induced Complex on Point Data

TL;DR

This paper introduces the graph induced complex, a new topological data analysis tool that efficiently captures the topology of point data using a graph-based approach, balancing complexity and accuracy.

Contribution

The paper proposes the graph induced complex, which combines advantages of Vietoris-Rips and witness complexes, enabling efficient topological inference from sparse samples.

Findings

Infers one-dimensional homology from sparse samples

Reconstructs 3D surfaces without Delaunay triangulation

Recovers persistent homology of compact sets

Abstract

The efficiency of extracting topological information from point data depends largely on the complex that is built on top of the data points. From a computational viewpoint, the most favored complexes for this purpose have so far been Vietoris-Rips and witness complexes. While the Vietoris-Rips complex is simple to compute and is a good vehicle for extracting topology of sampled spaces, its size is huge--particularly in high dimensions. The witness complex on the other hand enjoys a smaller size because of a subsampling, but fails to capture the topology in high dimensions unless imposed with extra structures. We investigate a complex called the {\em graph induced complex} that, to some extent, enjoys the advantages of both. It works on a subsample but still retains the power of capturing the topology as the Vietoris-Rips complex. It only needs a graph connecting the original sample points from which it builds a complex on the subsample thus taming the size considerably. We show that, using the graph induced complex one can (i) infer the one dimensional homology of a manifold from a very lean subsample, (ii) reconstruct a surface in three dimension from a sparse subsample without computing Delaunay triangulations, (iii) infer the persistent homology groups of compact sets from a sufficiently dense sample. We provide experimental evidences in support of our theory.