English

Clustering with Simplicial Complexes

Machine Learning 2023-03-15 v1 Signal Processing

Abstract

In this work, we propose a new clustering algorithm to group nodes in networks based on second-order simplices (aka filled triangles) to leverage higher-order network interactions. We define a simplicial conductance function, which on minimizing, yields an optimal partition with a higher density of filled triangles within the set while the density of filled triangles is smaller across the sets. To this end, we propose a simplicial adjacency operator that captures the relation between the nodes through second-order simplices. This allows us to extend the well-known Cheeger inequality to cluster a simplicial complex. Then, leveraging the Cheeger inequality, we propose the simplicial spectral clustering algorithm. We report results from numerical experiments on synthetic and real-world network data to demonstrate the efficacy of the proposed approach.

Keywords

Cite

@article{arxiv.2303.07646,
  title  = {Clustering with Simplicial Complexes},
  author = {Thummaluru Siddartha Reddy and Sundeep Prabhakar Chepuri and Pierre Borgnat},
  journal= {arXiv preprint arXiv:2303.07646},
  year   = {2023}
}
R2 v1 2026-06-28T09:15:36.731Z