Networks are all around us, we ourselves are individual unit of network of different kinds of social relationships. Neural networks, chemical and biological systems, internet and world-wide-web, socially interacting species are few examples of networks with highly interconnected large number of dynamic units. The basic way to apprehend the global properties of such system is to model these systems as graphs having nodes as individual units and links as interactions among them. However the modelling of such complex system stays challenging due to complex irregular structure which is dynamically evolving with time. Such challenging characteristics have initiated the revival of network-modelling as already available mathematical graph theory models turned out to be very far from such real-world needs. Hence the researchers have to come up with new approaches for the development of new models that can replicate the growth of the network and reproduce the behavioural and structural properties observed in real-world networks. The network modelling emphasises on developing a model to represent a complex systems as discussed above using relatively simple set of procedures or equations. When some network is given as an input to such models, the model itself learns some set of characteristic parameters and reconstructs another networks that mimics the input network as much as possible.
Pradumn Kumar Pandey has received his Bachelor of Technology and Ph.D. in Computer Science and Engineering from the Indian Institute of Technology Jodhpur in 2012 and 2018, respectively. He was an Institute Post Doctoral Fellow in the Department of Computer Science and Engineering at Indian Institute of Technology Kharagpur during May - September 2018 and worked as a DST INSPIRE Faculty in the Department of Computer Science and Engineering at Indian Institute of Technology Roorkee during October 2018- October 2019. He has been working as an assistant professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Roorkee since November 2019. His research areas include modeling complex networks, information diffusion on real networks, social security on online social networks, and network representation learning.