arXiv subjects

Alok Yadav

Publications and source records attributed to Alok Yadav.

11 recordsLinked to original sources

arXiv2026

Stochastic Networked Governance: Bridging Econophysics and Institutional Dynamics in a Positive-Sum Agent-Based Model

Traditional macroeconomic growth models rely on general equilibrium and continuous, frictionless institutional transitions, failing to account for the catastrophic structural collapses observed in empirical economic history. We propose the Stochastic Networked Governance (SNG) model, a discrete-time, agent-based framework that bridges econophysics, network science, and institutional economics. By defining jurisdictions through a binary institutional genome, the model formalizes institutional complementarity, endogenous growth, and the non-linear macroeconomic penalties of structural reform (the "J-Curve"). Using the CEPII Gravity Database and the IMF Systemic Banking Crises dataset, we move beyond theoretical topologies to execute an empirical historical simulation from 1970 to 2017 across the top 100 global economies. Through Monte Carlo ensembles, we demonstrate how scale-invariant exogenous shocks and spatial capital flight drive global phase transitions, exposing the mathematical mechanics of the 1989-1991 Soviet collapse, the Hub-Risk Paradigm, and the emergent resilience of spatially firewalled market networks.

physics.soc-ph↗

arXiv2026

The Simplicial Bridge: Mapping quantum multi-spin exchange to higher-order topological networks in continuous magnetic fields

The macroscopic dynamics of topological defects in magnetic materials are traditionally modeled using pairwise interactions. However, higher-order quantum exchange mechanisms - such as biquadratic and 4-spin ring exchange-play a critical role in strongly correlated systems. In this work, we introduce the "Simplicial Bridge," an exact analytical framework that maps these high-dimensional, non-linear Landau-Lifshitz partial differential equations onto generalized Kuramoto phase-oscillator networks operating on abstract simplicial complexes. We rigorously demonstrate that spatial overlap in the continuous limit natively generates higher-order topological forces without requiring a supportive discrete atomic lattice. Specifically, the overlap of 1D helimagnetic kinks generates 2-simplices (triadic forces), while the spatial compression of 2D skyrmion tails - governed by modified Bessel function asymptotics - generates true 3-simplices (tetradic forces). Furthermore, we establish that the higher-order spatial derivatives inherent to these multi-spin interactions provide an intrinsic energetic barrier that bypasses Derrick's Theorem, stabilizing 2D topological solitons without the strict need for Dzyaloshinskii-Moriya Interaction (DMI).

nlin.PS↗

arXiv2026

Emergent Topological Universality and Marginal Replica Symmetry Breaking in Gauge-Correlated Spin Glasses

Recent tensor-network samplings of modified Nishimori spin glasses have revealed robust finite-temperature critical transitions in two dimensions, defying the standard Edwards-Anderson lower critical dimension boundary ($d_{l}\approx2.5$). We present a theoretical framework demonstrating that the discrete $Z_{2}$ gauge constraints utilized to bypass Monte Carlo kinetic traps fundamentally alter the system's universality class. By mapping the algorithmic disorder distribution to the 2D Ising Conformal Field Theory (CFT), we prove the emergent spatial variance generates a fractional momentum operator that drives the dynamic upper critical dimension to zero ($d_{u}\rightarrow0$). This marginal topology dynamically suppresses the replica-coupling vertices, yielding an infinite-order Berezinskii-Kosterlitz-Thouless (BKT) transition and a non-integrable replicon divergence that predicts a massive instability toward 1-step Replica Symmetry Breaking (1-RSB). Leveraging a spectral Corner Transfer Matrix Renormalization Group (CTMRG) architecture up to macroscopic scales ($L=1024$), we quantitatively validate the topological scaling argument $\mathcal{G}((T-T_{c})\ln(L/L_{0}))$. By isolating the continuum field theory from microscopic lattice artifacts, we recover the fundamental lattice metric $L_{0}\approx 0.94$, unequivocally confirming the existence of a distinct, topologically driven spin-glass phase.

cond-mat.dis-nn↗

arXiv2019

Symmetry in cancer networks identified: Proposal for multi-cancer biomarkers

One of the most challenging problems in biomedicine and genomics is the identification of disease biomarkers. In this study, proteomics data from seven major cancers were used to construct two weighted protein-protein interaction (PPI) networks i.e., one for the normal and another for the cancer conditions. We developed rigorous, yet mathematically simple, methodology based on the degeneracy at -1 eigenvalues to identify structural symmetry or motif structures in network. Utilising eigenvectors corresponding to degenerate eigenvalues in the weighted adjacency matrix, we identified structural symmetry in underlying weighted PPI networks constructed using seven cancer data. Functional assessment of proteins forming these structural symmetry exhibited the property of cancer hallmarks. Survival analysis refined further this protein list proposing BMI, MAPK11, DDIT4, CDKN2A, and FYN as putative multi-cancer biomarkers. The combined framework of networks and spectral graph theory developed here can be applied to identify symmetrical patterns in other disease networks to predict proteins as potential disease biomarkers.

q-bio.MN↗

arXiv2019

Inhibition induced explosive synchronization in multiplex networks

To date, explosive synchronization (ES) is shown to be originated from either degree-frequency correlation or inertia of phase oscillators. Of late, it has been shown that ES can be induced in a network by adaptively controlled phase oscillators. Here we show that ES is a generic phenomenon and can occur in any network by appropriately multiplexing it with another layer. We devise an approach which leads to the occurrence of ES with hysteresis loop in a network upon its multiplexing with a negatively coupled (or inhibitory) layer. We discuss the impact of various structural properties of positively coupled (or excitatory) and inhibitory layer along with the strength of multiplexing in gaining control over the induced ES transition. This investigation is a step forward in highlighting the importance of multiplex framework not only in bringing novel phenomena which are not possible in an isolated network but also in providing more structural control over the induced phenomena.

nlin.AO↗

arXiv2017

Optimized evolution of networks for principal eigenvector localization

Network science is increasingly being developed to get new insights about behavior and properties of complex systems represented in terms of nodes and interactions. One useful approach is investigating localization properties of eigenvectors having diverse applications including disease-spreading phenomena in underlying networks. In this work, we evolve an initial random network with an edge rewiring optimization technique considering the inverse participation ratio as a fitness function. The evolution process yields a network having localized principal eigenvector. We analyze various properties of the optimized networks and those obtained at the intermediate stage. Our investigations reveal the existence of few special structural features of such optimized networks including the presence of a set of edges which are necessary for the localization, and rewiring only one of them leads to a complete delocalization of the principal eigenvector. Our investigation reveals that PEV localization is not a consequence of a single network property, and preferably requires co-existence of various distinct structural as well as spectral features.

nlin.AO↗

arXiv2017

Unveiling the Multi-fractal Structure of Complex Networks

The fractal nature of graphs has traditionally been investigated by using the nodes of networks as the basic units. Here, instead, we propose to concentrate on the graph edges, and introduce a practical and computationally not demanding method for revealing changes in the fractal behavior of networks, and particularly for allowing distinction between mono-fractal, quasi mono-fractal, and multi-fractal structures. We show that degree homogeneity plays a crucial role in determining the fractal nature of the underlying network, and report on six different protein-protein interaction networks along with their corresponding random networks. Our analysis allows to identify varying levels of complexity in the species.

physics.soc-ph↗

arXiv2016

Multilayer network decoding versatility and trust

In the recent years, the multilayer networks have increasingly been realized as a more realistic framework to understand emergent physical phenomena in complex real world systems. We analyze a massive time-varying social data drawn from the largest film industry of the world under multilayer network framework. The framework enables us to evaluate the versatility of actors, which turns out to be an intrinsic property of lead actors. Versatility in dimers suggests that working with different types of nodes are more beneficial than with similar ones. However, the triangles yield a different relation between type of co-actor and the success of lead nodes indicating the importance of higher order motifs in understanding the properties of the underlying system. Furthermore, despite the degree-degree correlations of entire networks being neutral, multilayering picks up different values of correlation indicating positive connotations like trust, in the recent years. Analysis of weak ties of the industry uncovers nodes from lower degree regime being important in linking Bollywood clusters. The framework and the tools used herein may be used for unraveling the complexity of other real world systems.

physics.soc-ph↗

arXiv2016

Dissortativity and duplications in Oral cancer

More than 300,000 new cases worldwide are being diagnosed with oral cancer annually. Complexity of oral cancer renders designing drug targets very difficult. We analyse protein-protein interaction network for the normal and oral cancer tissue and detect crucial changes in the structural properties of the networks in terms of the interactions of the hub proteins and the degree-degree correlations. Further analysis of the spectra of both the networks, while exhibiting universal statistical behavior, manifest distinction in terms of the zero degeneracy, providing insight to the complexity of the underlying system.

q-bio.MN↗

arXiv2015

Assortative and disassortative mixing investigated using the spectra of graphs

We investigate the impact of degree-degree correlations on the spectra of networks. Even though density distributions exhibit drastic changes depending on the (dis)assortative mixing and the network architecture, the short range correlations in eigenvalues exhibit universal RMT predictions. The long range correlations turn out to be a measure of randomness in (dis)assortative networks. The analysis further provides insight in to the origin of high degeneracy at the zero eigenvalue displayed by majority of the biological networks.

physics.soc-ph↗

arXiv2015

Origin and implications of zero degeneracy in networks spectra

Spectra of real world networks exhibit properties which are different from the random networks. One such property is the existence of a very high degeneracy at zero eigenvalues. In this work, we provide possible reasons behind occurrence of the zero degeneracy in various networks spectra. Comparison of zero degeneracy in protein-protein interaction networks of six different species and in their corresponding model networks sheds light in understanding the evolution of complex biological systems.

cond-mat.dis-nn↗