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Disparity-driven heterogeneous nucleation in finite-size adaptive networks

Akash Yadav1, Jan Fialkowski2,3, Rico Berner4, V. K. Chandrasekar5,*, and D. V. Senthilkumar1,†

  • 1School of Physics, Indian Institute of Science Education and Research, Thiruvananthapuram-695551, Kerala, India
  • 2Complexity Science Hub Vienna, Josefstädter Straße 39, 1080 Vienna, Austria
  • 3Center for Medical Data Science, Medical University Vienna, Spitalgasse 23, 1090 Vienna, Austria
  • 4Department of Physics, Humboldt-Universität zu Berlin, Newtonstraße 15, 12489 Berlin, Germany
  • 5Centre for Nonlinear Science & Engineering, School of Electrical & Electronics Engineering, SASTRA Deemed University, Thanjavur-613401, Tamil Nadu, India

  • *chandru25nld@gmail.com
  • †skumar@iisertvm.ac.in

Phys. Rev. E 109, L052301 – Published 14 May, 2024

DOI: https://doi.org/10.1103/PhysRevE.109.L052301

Abstract

Phase transitions are crucial in shaping the collective dynamics of a broad spectrum of natural systems across disciplines. Here, we report two distinct heterogeneous nucleation facilitating single step and multistep phase transitions to global synchronization in a finite-size adaptive network due to the trade off between time scale adaptation and coupling strength disparities. Specifically, small intracluster nucleations coalesce either at the population interface or within the populations resulting in the two distinct phase transitions depending on the degree of the disparities. We find that the coupling strength disparity largely controls the nature of phase transition in the phase diagram irrespective of the adaptation disparity. We provide a mesoscopic description for the cluster dynamics using the collective coordinates approach that brilliantly captures the multicluster dynamics among the populations leading to distinct phase transitions. Further, we also deduce the upper bound for the coupling strength for the existence of two intraclusters explicitly in terms of adaptation and coupling strength disparities. These insights may have implications across domains ranging from neurological disorders to segregation dynamics in social networks.

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