S
Sarika Jalan
Researcher at Indian Institute of Technology Indore
Publications - 180
Citations - 2618
Sarika Jalan is an academic researcher from Indian Institute of Technology Indore. The author has contributed to research in topics: Eigenvalues and eigenvectors & Multiplexing. The author has an hindex of 26, co-authored 157 publications receiving 2178 citations. Previous affiliations of Sarika Jalan include Academia Sinica & Max Planck Society.
Papers
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Journal ArticleDOI
Delay regulated explosive synchronization in multiplex networks
Ajay Deep Kachhvah,Sarika Jalan +1 more
TL;DR: In this paper, it is shown that a single layer with time-delayed intra-layer coupling may experience a different type of transition to synchronization, e.g. ES or continuous, depending on the values of time delay.
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The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging.
Harry J. Whitwell,Maria Giulia Bacalini,Oleg Blyuss,Oleg Blyuss,Shangbin Chen,Paolo Garagnani,Susan Yu. Gordleeva,Sarika Jalan,M. V. Ivanchenko,O. I. Kanakov,Valentina Kustikova,Inés P. Mariño,Iosif B. Meyerov,Ekkehard Ullner,Claudio Franceschi,Alexey Zaikin,Alexey Zaikin,Alexey Zaikin +17 more
TL;DR: This review considers how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease and how the latest techniques for generating biomarker models for disease prediction can be applied to as both biomarker platforms for aging.
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Random matrix analysis of network Laplacians
TL;DR: Analyses of random networks, scale-free networks and small-world networks show that the nearest neighbor spacing distribution of the Laplacian of these networks follow Gaussian orthogonal ensemble statistics of the random matrix theory.
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Randomness and preserved patterns in cancer network
TL;DR: In this paper, the authors analyzed the breast cancer network and its normal counterpart at the proteomic level and provided a benchmark for designing drugs which can target a subgraph instead of individual proteins.
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Spectral analysis of deformed random networks.
TL;DR: In this article, the spectral properties of sparsely connected random networks under the random matrix framework were studied and the spectral rigidity, measured by the Dyson-Mehta and Gaussian orthogonal ensemble statistics, depends upon the deformation of the network from perfect community structure.