Pattern formation and transition in complex networks

TL;DR

This paper investigates how adding random links to regular networks affects the stability and types of dynamical patterns, revealing transitions from spiral waves to synchronization and chimera-like states.

Contribution

It systematically analyzes the impact of network structure changes on pattern transitions, highlighting the significance of local versus long-distance links.

Findings

Short-distance links near the spiral tip significantly influence wave patterns.

Increasing randomness induces pattern transitions including synchronization and chimera states.

Network dynamics are highly sensitive to small structural variations near transition points.

Abstract

Dynamical patterns in complex networks of coupled oscillators are both of theoretical and practical interest, yet to fully reveal and understand the interplay between pattern emergence and network structure remains to be an outstanding problem. A fundamental issue is the effect of network structure on the stability of the patterns. We address this issue by using the setting where random links are systematically added to a regular lattice and focusing on the dynamical evolution of spiral wave patterns. As the network structure deviates more from the regular topology (so that it becomes increasingly more complex), the original stable spiral wave pattern can disappear and a different type of pattern can emerge. Our main findings are the following. (1) Short-distance links added to a small region containing the spiral tip can have a more significant effect on the wave pattern than long-distance connections. (2) As more random links are introduced into the network, distinct pattern transitions can occur, which include the transition of spiral wave to global synchronization, to a chimera-like state, and then to a pinned spiral wave. (3) Around the transitions the network dynamics is highly sensitive to small variations in the network structure in the sense that the addition of even a single link can change the pattern from one type to another. These findings provide insights into the pattern dynamics in complex networks, a problem that is relevant to many physical, chemical, and biological systems.