The simpliciality of higher-order networks

TL;DR

This paper introduces the concept of 'simpliciality' to better understand inclusion in higher-order networks, revealing that real systems often do not conform to existing models and suggesting new directions for network modeling.

Contribution

It proposes a new measure of 'simpliciality' for higher-order networks and demonstrates its empirical relevance and the limitations of current generative models.

Findings

Real systems rarely are fully simplicial or non-simplicial.

Existing models struggle to replicate the inclusion structures observed.

New modeling approaches are needed for better representation.

Abstract

Higher-order networks are widely used to describe complex systems in which interactions can involve more than two entities at once. In this paper, we focus on inclusion within higher-order networks, referring to situations where specific entities participate in an interaction, and subsets of those entities also interact with each other. Traditional modeling approaches to higher-order networks tend to either not consider inclusion at all (e.g., hypergraph models) or explicitly assume perfect and complete inclusion (e.g., simplicial complex models). To allow for a more nuanced assessment of inclusion in higher-order networks, we introduce the concept of "simpliciality" and several corresponding measures. Contrary to current modeling practice, we show that empirically observed systems rarely lie at either end of the simpliciality spectrum. In addition, we show that generative models fitted to these datasets struggle to capture their inclusion structure. These findings suggest new modeling directions for the field of higher-order network science.