Antagonistic Phenomena in Network Dynamics

TL;DR

This paper reviews recent discoveries of antagonistic and coexistence phenomena in network dynamics, highlighting their implications across natural, social, and engineered systems, and proposing a unified framework for understanding these complex behaviors.

Contribution

It provides a comprehensive review of diverse antagonistic phenomena in network systems and introduces a unified perspective to understand their collective and decentralized origins.

Findings

Examples include negative compressibility, Braess paradox, and remote synchronization.

Highlights the collective nature of these phenomena across different network types.

Suggests a broader class of network behaviors for future exploration.

Abstract

Recent research on the network modeling of complex systems has led to a convenient representation of numerous natural, social, and engineered systems that are now recognized as networks of interacting parts. Such systems can exhibit a wealth of phenomena that not only cannot be anticipated from merely examining their parts, as per the textbook definition of complexity, but also challenge intuition even when considered in the context of what is now known in network science. Here we review the recent literature on two major classes of such phenomena that have far-reaching implications: (i) antagonistic responses to changes of states or parameters and (ii) coexistence of seemingly incongruous behaviors or properties -- both deriving from the collective and inherently decentralized nature of the dynamics. They include effects as diverse as negative compressibility in engineered materials, rescue interactions in biological networks, negative resistance in fluid networks, and the Braess paradox occurring across transport and supply networks. They also include remote synchronization, chimera states and the converse of symmetry breaking in brain, power-grid and oscillator networks as well as remote control in biological and bio-inspired systems. By offering a unified view of these various scenarios, we suggest that they are representative of a yet broader class of unprecedented network phenomena that ought to be revealed and explained by future research.