{"id":7,"date":"2024-04-02T12:00:47","date_gmt":"2024-04-02T12:00:47","guid":{"rendered":"https:\/\/faculty.iisertvm.ac.in\/skumar\/?page_id=7"},"modified":"2026-01-22T06:33:40","modified_gmt":"2026-01-22T06:33:40","slug":"about","status":"publish","type":"page","link":"https:\/\/faculty.iisertvm.ac.in\/skumar\/index.php\/about\/","title":{"rendered":"About"},"content":{"rendered":"\n
\n\n Research Associate position under the ANRF project will open soon. Potential candidates with Ph.D degree can send their CV and research proposal to skumar@iisertvm.ac.in\n<\/marquee>\n\n\n\n
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Research Highlights<\/h3>\n\n\n\n
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Mitigating cascades in coevolving networks with targeted rewiring (2025)<\/h5><\/div>\n\n\n\n
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We study how network structure and opinions evolve together by combining a threshold-based contagion model with a targeted rewiring strategy. Instead of allowing all non-adopting nodes to rewire, only key \u201csuperspreader\u201d nodes rewire their links to inhibit adoption cascades. Both theory and simulations show that this selective rewiring efficiently contains spreading while drastically reducing the number of rewiring operations. The approach minimizes structural disruption, offering a cost-effective and realistic way to stabilize adaptive networks.<\/p>\n\n\n\n

Singh, K., Thirumurugan, K., Chandrasekar, V. K., & Senthilkumar, D. V. (2025). Mitigating cascades in coevolving networks with targeted rewiring. Physical Review E<\/em>, 112<\/em>(5), L052303.<\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n

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Graph coloring framework to mitigate cascading failure in complex networks<\/strong> (2025)<\/h5><\/div>\n\n\n\n
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Cascading failures threaten the stability of complex systems. We propose a robust, graph-coloring-based framework to strategically identify a minimal set of critical nodes, using only local network topology, whose protection ensures near-complete survivability of the network. This approach effectively contains failure propagation, preserves network integrity, and outperforms existing strategies across diverse network types and scenarios.<\/p>\n\n\n\n

Singh, K., Chandrasekar, V.K., Zou, W. et al.<\/em> Graph coloring framework to mitigate cascading failure in complex networks. Commun Phys<\/em> 8<\/strong>, 170 (2025). https:\/\/doi.org\/10.1038\/s42005-025-02089-y<\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n

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Higher-order interaction induced chimeralike state in a bipartite network<\/h5><\/div>\n\n\n\n
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We report higher-order coupling induced stable chimeralike state in a bipartite network of coupled phase oscillators without any time-delay in the coupling. We show that the higher-order interaction breaks the symmetry of the homogeneous synchronized state to facilitate the manifestation of symmetry breaking chimeralike state. In particular, such symmetry breaking manifests only when the pairwise interaction is attractive and higher-order interaction is repulsive, and vice versa. Further, we also demonstrate the increased degree of heterogeneity promotes homogeneous symmetric states in the phase diagram by suppressing the asymmetric chimeralike state. We deduce the low-dimensional evolution equations for the macroscopic order parameters using Ott-Antonsen ansatz and obtain the bifurcation curves from them using the software xppaut, which agrees very well with the simulation results. We also deduce the analytical stability conditions for the incoherent state, in-phase and out-of-phase synchronized states, which match with the bifurcation curves.<\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n

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Disparity-driven heterogeneous nucleation in finite-size adaptive networks<\/h5><\/div>\n\n\n\n
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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.<\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n

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Exotic Swarming dynamics of High-Dimensional Swarmalators<\/h5><\/div>\n\n\n\n
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Swarmalators are oscillators that can swarm as well as sync via a dynamic balance between their spatial proximity and phase similarity. Swarmalator models employed so far in the literature comprise only one-dimensional phase variables to represent the intrinsic dynamics of the natural collectives. Nevertheless, the latter can indeed be represented more realistically by high-dimensional phase variables. For instance, the alignment of velocity vectors in a school of fish or a flock of birds can be more realistically set up in three-dimensional space, while the alignment of opinion formation in population dynamics could be multidimensional, in general. We present a generalized \ud835\udc37-dimensional swarmalator model, which more accurately captures self-organizing behaviors of a plethora of real-world collectives by self-adaptation of high-dimensional spatial and phase variables. For a more sensible visualization and interpretation of the results, we restrict our simulations to three-dimensional spatial and phase variables. Our model provides a framework for modeling complicated processes such as flocking, schooling of fish, cell sorting during embryonic development, residential segregation, and opinion dynamics in social groups. We demonstrate its versatility by capturing the maneuvers of a school of fish, qualitatively and quantitatively, by a suitable extension of the original model to incorporate appropriate features besides a gallery of its intrinsic self-organizations for various interactions. We expect the proposed high-dimensional swarmalator model to be potentially useful in describing swarming systems and programmable and reconfigurable collectives in a wide range of disciplines, including the physics of active matter, developmental biology, sociology, and engineering.<\/p>\n<\/div><\/div>\n<\/div><\/div>\n\n\n\n

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Effect of higher-order interactions on chimera states in two populations of Kuramoto oscillators<\/strong><\/h5><\/div>\n\n\n\n
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We investigate the effect of the fraction of pairwise and higher-order interactions on the emergent dynamics of the two populations of globally coupled Kuramoto oscillators with phase-lag parameters. We find that the stable chimera exists between saddle-node and Hopf bifurcations, while the breathing chimera lives between Hopf and homoclinic bifurcations in the two-parameter phase diagrams.We investigate the effect of the fraction of pairwise and higher-order interactions on the emergent dynamics of the two populations of globally coupled Kuramoto oscillators with phase-lag parameters. We find that the stable chimera exists between saddle-node and Hopf bifurcations, while the breathing chimera lives between Hopf and homoclinic bifurcations in the two-parameter phase diagrams.<\/p>\n<\/div><\/div>\n<\/div><\/div>\n<\/div><\/div>

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Our Interest Areas<\/h3>\n\n\n\n
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Complex Networks<\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

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Synchronization Phenomena <\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

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Ageing Transitions in Phase Oscillators<\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n\n\n\n

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Swarming Dynamics<\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

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Adaptive Networks <\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div>\n\n\n\n

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Population Ecology<\/a><\/strong><\/p>\n<\/div><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

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About logo: <\/strong>The butterfly logo embodies the profound concept that complex and chaotic processes can give rise to astonishing beauty. Much like the intricate wings of a butterfly emerge from chaotic trajectories within a Lorenz attractor, our logo’s design symbolizes this convergence of chaos and creativity. The wings, crafted from the dynamic patterns of the Lorenz attractor, reflect the intricate dance of unpredictability. They are intricately connected to the main body, which represents a complex network, symbolizing the interconnectedness of diverse elements within our field. Together, these elements converge to create something truly beautiful, enriching our understanding of nature’s complexities and inspiring awe in its intricate design.

~ Karan & Akash<\/p>\n<\/div>\n\n\n\n

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