STruD: Truss Decomposition of Simplicial Complexes

TL;DR

This paper introduces STruD, a novel memory-efficient algorithm for truss decomposition of simplicial complexes, enabling advanced structural analysis and topological data analysis through persistent homology.

Contribution

It generalizes graph truss decomposition to simplicial complexes and develops a scalable, memory-aware algorithm for efficient computation.

Findings

STruD efficiently computes truss decompositions on large datasets.

The approach reveals structural properties of simplicial complexes.

It enables topological analysis via persistent homology.

Abstract

A simplicial complex is a generalization of a graph: a collection of n-ary relationships (instead of binary as the edges of a graph), named simplices. In this paper, we develop a new tool to study the structure of simplicial complexes: we generalize the graph notion of truss decomposition to complexes, and show that this more powerful representation gives rise to different properties compared to the graph-based one. This power, however, comes with important computational challenges derived from the combinatorial explosion caused by the downward closure property of complexes. Drawing upon ideas from itemset mining and similarity search, we design a memory-aware algorithm, dubbed STruD, which is able to efficiently compute the truss decomposition of a simplicial complex. STruD adapts its behavior to the amount of available memory by storing intermediate data in a compact way. We then devise a variant that computes directly the n simplices of maximum trussness. By applying STruD to several datasets, we prove its scalability, and provide an analysis of their structure. Finally, we show that the truss decomposition can be seen as a filtration, and as such it can be used to study the persistent homology of a dataset, a method for computing topological features at different spatial resolutions, prominent in Topological Data Analysis.