In this work we propose a parametric network model for generation of complex networks that can inherit statistical properties of real networks. The model is based on different growth processes that are observed in different social contexts, for example, preferential attachment, random attachment with local growth. Further, the parametric model approach for generation of networks is extended and employed into solving the problem of structural reconstruction of real scale-free networks. In this attempt, a 2-parameter network generation model, called Network-Reconstruction-Model (NRM) is developed. A reconstruction technique is introduced to reconstruct a given real scale-free network by finding optimal values of the model parameters, utilizing the power-law exponent of the degree distribution of the real network, such that the corresponding model network inherit multiple structural properties of the real network. The performance of all the models in order to inherit properties of real networks is tested with different examples of real networks. The efficiency of NRM and the proposed reconstruction technique in order to solve the structural reconstruction problem are compared with some existing network models. Further, NRM is extended to do structural reconstruction of signed networks also.
About the Speaker
Pradumn Kumar Pandey received the B.Tech. and PhD degrees in Computer Science and Engineering from IIT Jodhpur in 2012 and 2018, respectively. Currently, he is working as an Institute Post-Doctoral Fellow in the Department of Computer Science and Engineering, IIT Kharagpur.
His research areas include modeling of complex networks, information diffusion on real networks, and network representation learning.