arXiv subjects

Arpan Banerjee

Publications and source records attributed to Arpan Banerjee.

3 recordsLinked to original sources

arXiv2026

On the role of higher-order interactions towards first synchronization time

This study investigates transient collective dynamics, with a focus on how higher-order interactions impact the time required to reach steady-state synchronization. Assuming a large ensemble of deterministic and globally coupled Kuramoto oscillators with Cauchy-distributed natural frequencies, an expression for the first synchronization time is derived using the Ott-Antonsen ansatz. Subsequent numerics reveal that (i) increasing the coupling strengths for a fixed interaction order accelerates the transition to synchronization and (ii) increasing the interaction order for fixed interaction strength produces non-monotonic behavior. In particular, the inclusion of triadic interactions generally accelerates synchronization, whereas further higher-order interactions progressively delay convergence to the steady state, in some regimes even falling below the pairwise level. Ultimately, for very large interaction orders, the dynamics revert to pairwise-like behavior. Simulations of the system equations for different parameter combinations support these observations, while the asymptotic case is interpreted through the nonlinear structure of the order-parameter dynamics.

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arXiv2025

Emergent synchrony in oscillator networks with adaptive arbitrary-order interactions

Dynamics of complex systems are often driven by interactions that extend beyond pairwise links, underscoring the need to establish a correspondence between interpretable system parameters and emergent phenomena in hypergraph-based networks. The current work formulates an adaptive Kuramoto model that incorporates hyperedges of arbitrary order and explores their effects on synchronization. By deriving the exact order parameter dynamics in the thermodynamic limit, analytical expressions governing the collective dynamics are obtained. Subsequent numerics confirm the analytical predictions, in addition to capturing qualitatively different dynamical regimes and phase transitions. Further investigations based on order parameter distributions demonstrate how fluctuations, arising due to finite system size, can influence the long-term system dynamics. These results provide important insights and can have diverse applications, such as designing optimal surgical procedures for drug-resistant epilepsy and identifying the sources of rumours in a social network.

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arXiv2019

Generative framework for dimensionality reduction of large scale network of non-linear dynamical systems driven by external input

Several studies have proposed constraints under which a low dimensional representation can be derived from large-scale real-world networks exhibiting complex nonlinear dynamics. Typically, these representations are formulated under certain assumptions, such as when solutions converge to attractor states using linear stability analysis or using projections of large-scale dynamical data into a set of lower dimensional modes that are selected heuristically. Here, we propose a generative framework for selection of lower dimensional modes onto which the entire network dynamics can be projected based on the symmetry of the input distribution for a large-scale network driven by external inputs, thus relaxing the heuristic selection of modes made in the earlier reduction approaches. The proposed mode reduction technique is tractable analytically and applied to different kinds of real-world large-scale network scenarios with nodes comprising of a) Van der Pol oscillators b) Hindmarsh-Rose neurons. These two demonstrations elucidate how order parameter is conserved at original and reduced descriptions thus validating our proposition.

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