C
Camellia Sarkar
Researcher at Indian Institute of Technology Indore
Publications - 20
Citations - 267
Camellia Sarkar is an academic researcher from Indian Institute of Technology Indore. The author has contributed to research in topics: Network theory & Randomness. The author has an hindex of 9, co-authored 20 publications receiving 226 citations.
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Spectral properties of complex networks
Camellia Sarkar,Sarika Jalan +1 more
TL;DR: In this article, the authors present an account of the major works on spectra of adjacency matrices drawn on networks and the basic understanding attained so far, divided the review under three sections: (a) extremal eigenvalues, (b) bulk part of the spectrum, and (c) degenerate eigenvalue, based on the intrinsic properties of eigen values and the phenomena they capture.
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Uncovering randomness and success in society.
TL;DR: The analysis carried forth in this work, using a conjoined framework of complex network theory and random matrix theory, aims to quantify the elements that determine the fitness of an individual node and the factors that contribute to the robustness of a network.
Journal ArticleDOI
Spectral properties of complex networks
Camellia Sarkar,Sarika Jalan +1 more
TL;DR: This review presents an account of the major works done on spectra of adjacency matrices drawn on networks and the basic understanding attained so far, divided under three sections: extremal eigenvalues, bulk part of the spectrum, and degenerate eigen Values, based on the intrinsic properties of eigen values and the phenomena they capture.
Journal ArticleDOI
Multilayer network decoding versatility and trust
TL;DR: A massive time-varying social data drawn from the largest film industry of the world under multilayer network framework is analyzed to evaluate the versatility of actors, which turns out to be an intrinsic property of lead actors.
Journal ArticleDOI
Quantifying randomness in protein–protein interaction networks of different species: A random matrix approach
TL;DR: It is demonstrated that spectral rigidity, which quantifies long range correlations in eigenvalues, for all protein–protein interaction networks follow random matrix prediction up to certain ranges indicating randomness in interactions.