Interaction network effects on position- and velocity-based models of collective motion

TURGUT A. E., Boz I. C., Okay I. E., Ferrante E., Huepe C.

JOURNAL OF THE ROYAL SOCIETY INTERFACE, cilt.17, sa.169, 2020 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 17 Sayı: 169
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1098/rsif.2020.0165
  • Dergi Adı: JOURNAL OF THE ROYAL SOCIETY INTERFACE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Agricultural & Environmental Science Database, Animal Behavior Abstracts, BIOSIS, CAB Abstracts, Compendex, EMBASE, INSPEC, MEDLINE, Veterinary Science Database
  • Anahtar Kelimeler: collective motion, Vicsek model, Active-Elastic model, interaction topology, complex networks, order-disorder transition, PHASE-TRANSITION
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

We study how the structure of the interaction network affects self-organized collective motion in two minimal models of self-propelled agents: the Vicsek model and the Active-Elastic (AE) model. We perform simulations with topologies that interpolate between a nearest-neighbour network and random networks with different degree distributions to analyse the relationship between the interaction topology and the resilience to noise of the ordered state. For the Vicsek case, we find that a higher fraction of random connections with homogeneous or power-law degree distribution increases the critical noise, and thus the resilience to noise, as expected due to small-world effects. Surprisingly, for the AE model, a higher fraction of random links with power-law degree distribution can decrease this resilience, despite most links being long-range. We explain this effect through a simple mechanical analogy, arguing that the larger presence of agents with few connections contributes localized low-energy modes that are easily excited by noise, thus hindering the collective dynamics. These results demonstrate the strong effects of the interaction topology on self-organization. Our work suggests potential roles of the interaction network structure in biological collective behaviour and could also help improve decentralized swarm robotics control and other distributed consensus systems.