Conflicting attachment and the growth of bipartite networks

TL;DR

This paper introduces a stochastic growth model for bipartite networks with conflicting attachment preferences, capturing complex structures like nestedness and modularity seen in real-world host-parasite systems.

Contribution

It proposes a novel conflicting attachment model that explains the emergence of realistic bipartite network structures influenced by opposing agent interests.

Findings

Networks exhibit nestedness and modularity similar to empirical data

Conflicting preferences shape network topology and complexity

Model captures realistic patterns in host-parasite interactions

Abstract

Simple growth mechanisms have been proposed to explain the emergence of seemingly universal network structures. The widely-studied model of preferential attachment assumes that new nodes are more likely to connect to highly connected nodes. Preferential attachment explains the emergence of scale-free degree distributions within complex networks. Yet, it is incompatible with many network systems, particularly bipartite systems in which two distinct types of agents interact. For example, the addition of new links in a host-parasite system corresponds to the infection of hosts by parasites. Increasing connectivity is beneficial to a parasite and detrimental to a host. Therefore, the overall network connectivity is subject to conflicting pressures. Here, we propose a stochastic network growth model of conflicting attachment, inspired by a particular kind of parasite-host interactions: that of viruses interacting with microbial hosts. The mechanism of network growth includes conflicting preferences to network density as well as costs involved in modifying the network connectivity according to these preferences. We find that the resulting networks exhibit realistic patterns commonly observed in empirical data, including the emergence of nestedness, modularity, and nested-modular structures that exhibit both properties. We study the role of conflicting interests in shaping network structure and assess opportunities to incorporate greater realism in linking growth process to pattern in systems governed by antagonistic and mutualistic interactions.