# Nestedness in complex networks: Observation, emergence, and implications

## TL;DR

This paper reviews the concept of nestedness in complex networks, exploring its observation, theoretical explanations, and implications across ecological and socio-economic systems, emphasizing its emergence in various network types and interdisciplinary significance.

## Contribution

It provides the first comprehensive unification of nestedness studies in bipartite and unipartite networks, integrating insights from multiple disciplines.

## Key findings

- Nestedness occurs in both bipartite and unipartite networks.
- Various mechanisms explain the emergence of nestedness.
- Nestedness impacts system stability and feasibility.

## Abstract

The observed architecture of ecological and socio-economic networks differs significantly from that of random networks. From a network science standpoint, non-random structural patterns observed in real networks call for an explanation of their emergence and an understanding of their potential systemic consequences. This article focuses on one of these patterns: nestedness. Given a network of interacting nodes, nestedness can be described as the tendency for nodes to interact with subsets of the interaction partners of better-connected nodes. Known since more than $80$ years in biogeography, nestedness has been found in systems as diverse as ecological mutualistic organizations, world trade, inter-organizational relations, among many others. This review article focuses on three main pillars: the existing methodologies to observe nestedness in networks; the main theoretical mechanisms conceived to explain the emergence of nestedness in ecological and socio-economic networks; the implications of a nested topology of interactions for the stability and feasibility of a given interacting system. We survey results from variegated disciplines, including statistical physics, graph theory, ecology, and theoretical economics. Nestedness was found to emerge both in bipartite networks and, more recently, in unipartite ones; this review is the first comprehensive attempt to unify both streams of studies, usually disconnected from each other. We believe that the truly interdisciplinary endeavour -- while rooted in a complex systems perspective -- may inspire new models and algorithms whose realm of application will undoubtedly transcend disciplinary boundaries.