Spectral Detection of Simplicial Communities via Hodge Laplacians

TL;DR

This paper introduces a spectral method based on the Hodge Laplacian to detect communities in higher-order networks modeled as simplicial complexes, extending spectral clustering to multi-node interactions.

Contribution

It proposes a novel algorithm for extracting simplicial communities using the Hodge Laplacian spectrum and introduces an inference method to predict higher-order interactions from pairwise data.

Findings

The spectral algorithm successfully identifies simplicial communities in benchmarks and real data.

The inference method accurately predicts higher-order interactions that align with ground-truth communities.

Persistent simplicial communities can be observed across various thresholds in collaboration networks.

Abstract

Despite being a source of rich information, graphs are limited to pairwise interactions. However, several real-world networks such as social networks, neuronal networks, etc., involve interactions between more than two nodes. Simplicial complexes provide a powerful mathematical framework to model such higher-order interactions. It is well known that the spectrum of the graph Laplacian is indicative of community structure, and this relation is exploited by spectral clustering algorithms. Here we propose that the spectrum of the Hodge Laplacian, a higher-order Laplacian defined on simplicial complexes, encodes simplicial communities. We formulate an algorithm to extract simplicial communities (of arbitrary dimension). We apply this algorithm to simplicial complex benchmarks and to real higher-order network data including social networks and networks extracted using language or text processing tools. However, datasets of simplicial complexes are scarce, and for the vast majority of datasets that may involve higher-order interactions, only the set of pairwise interactions are available. Hence, we use known properties of the data to infer the most likely higher-order interactions. In other words, we introduce an inference method to predict the most likely simplicial complex given the community structure of its network skeleton. This method identifies as most likely the higher-order interactions inducing simplicial communities that maximize the adjusted mutual information measured with respect to ground-truth community structure. Finally, we consider higher-order networks constructed through thresholding the edge weights of collaboration networks (encoding only pairwise interactions) and provide an example of persistent simplicial communities that are sustained over a wide range of the threshold.