Chakresh Kr. Singh

Chakresh Kr. Singh

Complex Networks, Non-Linear Dynamics, Agent Based Modelling and Machine Learning

Sitemap

A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

Pages

Posts

Future Blog Post

less than 1 minute read

Published:

This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

Characterization of functional brain networks and emotional centers using the complex networks techniques

Published in Proceedings of the International Conference on Complex Networks and Their Applications, Lisbon, Portugal, 2018

In this work, we extract functional brain networks using coherence measures on EEG recordings of film clips from popular Indian Bollywood movies representing nine Rasas in the Indian Natyasastra. Structural and functional network measures were computed for these brain networks, averaging over a range of significant edge weights, in different brainwave frequency bands.

Download here

Structure and Evolution of Indian Physics Co-authorship Networks

Published in Scientometrics, 2019

We trace the evolution of Indian physics community from 1919 to 2013 by analyzing the co-authorship network constructed from papers published by authors in India in American Physical Society (APS) journals. We make inferences on India’s contribution to different branches of Physics and identify the most influential Indian physicists at different time periods. The relative contribution of India to global physics publication (research) and its variation across subfields of physics is assessed. We extract the changing collaboration pattern of authors between Indian physicists through various network measures. We study the evolution of Indian physics communities and trace the mean life and stationarity of communities by size in different APS journals. We map the transition of authors between communities of different sizes from 1970 to 2013, capturing their birth, growth, merger and collapse. We find that Indian–Foreign collaborations are increasing at a faster pace compared to the Indian–Indian. We observe that the degree distribution of Indian collaboration networks follows the power law, with distinct patterns between Physical Review A, B and E, and high energy physics journals Physical Review C and D, and Physical Review Letters. In almost every measure, we observe strong structural differences between low-energy and high-energy physics journals.

Download here

Exploring the role and nature of interactions between Institutes in a Local Affiliation Network

Published in Proceedings of the 10th Conference on Complex Networks CompleNet, 2019

Here we study the collaboration and citation network between Indian Institutes from publications in American Physical Society (APS) journals between 1970–2013. We investigate the role of geographic proximity on the network structure and find that it is the characteristics of the Institution, rather than the geographic distance, that play a dominant role in collaboration networks. We find that Institutions with better federal funding dominate the network topology and play a crucial role in overall research output. We find that the citation flow across different category of institutions is strongly linked to the collaborations between them. We have estimated the knowledge flow in and out of Institutions and identified the top knowledge source and sinks.

Download here

Evolution of Interdependent Co-authorship and Citation Networks

Published in Scientometrics, 2020

Studies of bibliographic data suggest a strong correlation between the growth of citation networks and their corresponding co-authorship networks. We explore the interdependence between evolving citation and co-authorship networks focused on the publications, by Indian authors, in American Physical Society journals between 1970 and 2013. We record interactions between each possible pair of authors in two ways: first, by tracing the change in citations, they exchanged and, second, by tracing the shortest path between authors in the co-authorship network. We create these data for every year of the period of our analysis. We use probability methods to quantify the correlation between citations and shortest paths, and the effect on the dynamics of the citation-co-authorship system. We find that author pairs who have a co-authorship distance 𝑑≤3 significantly affect each other’s citations, but that this effect falls off rapidly for longer distances in the co-authorship network. The exchange of citation between pairs with 𝑑=1 exhibits a sudden increase at the time of first co-authorship events and decays thereafter, indicating an ageing effect in collaboration. This behaviour suggests that the dynamics of the co-authorship network appear to be driving those of the citation network rather than vice versa. Moreover, the majority of citations received by most authors are due to reciprocal citations from current, or past, co-authors. We conclude that, in order to answer questions on the nature and dynamics of scientific collaboration, it is necessary to study both co-authorship and citation network simultaneously.

Download here

Quantifying the rise and fall of scientific fields

Published in PLOS ONE, 2022

Science advances by pushing the boundaries of the adjacent possible. While the global scientific enterprise grows at an exponential pace, at the mesoscopic level the exploration and exploitation of research ideas are reflected through the rise and fall of research fields. The empirical literature has largely studied such dynamics on a case-by-case basis, with a focus on explaining how and why communities of knowledge production evolve. Although fields rise and fall on different temporal and population scales, they are generally argued to pass through a common set of evolutionary stages. To understand the social processes that drive these stages beyond case studies, we need a way to quantify and compare different fields on the same terms. In this paper we develop techniques for identifying common patterns in the evolution of scientific fields and demonstrate their usefulness using 1.5 million preprints from the arXiv repository covering 175 research fields spanning Physics, Mathematics, Computer Science, Quantitative Biology and Quantitative Finance. We show that fields consistently follow a rise and fall pattern captured by a two parameters right-tailed Gumbel temporal distribution. We introduce a field-specific re-scaled time and explore the generic properties shared by articles and authors at the creation, adoption, peak, and decay evolutionary phases. We find that the early phase of a field is characterized by disruptive works mixing of cognitively distant fields written by small teams of interdisciplinary authors, while late phases exhibit the role of specialized, large teams building on the previous works in the field. This method provides foundations to quantitatively explore the generic patterns underlying the evolution of research fields in science, with general implications in innovation studies.

Download here

talks

Statistical Physics and Complex Networks

Published:

This talk was focussed on Statistical Mechanics of Complex Networks. The motive of the talk was to highlight and appreciate the robustness of Statistical Mechanical methods in the field of Complex Networks. I mainly discussed the following paper (https://journals.aps.org/pre/abstract/10.1103/PhysRevE.70.066117) and some other interest aspects of Complex Networks when viewed from a physicist’s perspective

teaching

Teaching Assistant

Undergraduate and Masters courses, Indian Institute of Technology Gandhinagar, Physics Discipline, 2019

List of Courses for which I have been the Teaching Assistant uptill Sept. 2019. This required me to take tutorial classes and grade the assignments and quizes.