IntComplex for high-order interactions

TL;DR

IntComplex introduces a novel topological framework using homology theory to analyze high-order interactions in complex networks, addressing limitations of traditional graph models in capturing multi-entity interactions.

Contribution

It proposes a comprehensive framework leveraging homology theory to characterize and analyze high-order interactions, including transitions between different interaction dimensions, with stability guarantees.

Findings

Defines IntComplex as a collection of interactions represented by binary trees.

Introduces p-layer and multilayer homology for structural analysis across dimensions.

Establishes persistent homology with stability for robust analysis.

Abstract

Graphs serve as powerful tools for modeling pairwise interactions in diverse fields such as biology, material science, and social networks. However, they inherently overlook interactions involving more than two entities. Simplicial complexes and hypergraphs have emerged as prominent frameworks for modeling many-body interactions; nevertheless, they exhibit limitations in capturing specific high-order interactions, particularly those involving transitions from $n$-interactions to $m$-interactions. Addressing this gap, we propose IntComplex as an innovative framework to characterize such high-order interactions comprehensively. Our framework leverages homology theory to provide a quantitative representation of the topological structure inherent in such interactions. IntComplex is defined as a collection of interactions, each of which can be equivalently represented by a binary tree. Drawing inspiration from GLMY homology, we introduce homology for the detailed analysis of structural patterns formed by interactions across adjacent dimensions, $p$-layer homology to elucidate loop structures within $p$-interactions in specific dimensions, and multilayer homology to analyze loop structures of interactions across multiple dimensions. Furthermore, we introduce persistent homology through a filtration process and establish its stability to ensure robust quantitative analysis of these complex interactions. The proposed IntComplex framework establishes a foundational paradigm for the analysis of topological properties in high-order interactions, presenting significant potential to drive forward the advancements in the domain of complex network analysis.