Giridhar Maji
Academic editor at PlosOne in the areas of complex networks. Editorial review board member for IGI global IJISP. Referee (Reviewer) for many high quality computer science journals like IEEE Transactions TCAS, Springer Nonlinear Dynamics, IEI Series B, Elsevier Expert Systems with Applications, Journal of Computational Science, IGI Global IJSI, IJISP, and many others. Working as a faculty in the Dept. Of Technical Education, Training and Skill Development, Govt. Of West Bengal, India.
Worked with TCS and Cognizant Technology Solutions for around 7 years in different capacities in IT/ITes projects. B Tech In 2007 from NIT Durgapur. M Tech in Computer science from Calcutta University in 2015. PhD from University of Calcutta, India in 2023. Research Interests include Data mining, Data warehousing, Information security, steganography and complex network analysis.
Supervisors: Prof. Soumya Sen
Worked with TCS and Cognizant Technology Solutions for around 7 years in different capacities in IT/ITes projects. B Tech In 2007 from NIT Durgapur. M Tech in Computer science from Calcutta University in 2015. PhD from University of Calcutta, India in 2023. Research Interests include Data mining, Data warehousing, Information security, steganography and complex network analysis.
Supervisors: Prof. Soumya Sen
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Papers by Giridhar Maji
emerged as an important research challenge to control the spread of (mis)information or infectious diseases. Researchers have proposed many centrality measures to identify the influential nodes (spreaders), in the past few years. Still, most of them have not considered the importance of the edges in unweighted networks. To address this issue, we propose a novel centrality measure to identify the spreading ability of the Influential Spreaders using Potential Edge Weight (IS-PEW). Considering the connectivity structure, ability of information exchange, and the importance of neighboring nodes, we measure the potential edge weight. The ranking similarity of spreaders identified by the IS-PEW method and the baseline centrality methods is compared with Susceptible–Infectious–Recovered (SIR) epidemic simulator using Kendall’s rank correlation. Considering six different real networks, the spreading ability of the top-ranking spreaders is also compared for five different percentages of top-ranking node sets.
emerged as an important research challenge to control the spread of (mis)information or infectious diseases. Researchers have proposed many centrality measures to identify the influential nodes (spreaders), in the past few years. Still, most of them have not considered the importance of the edges in unweighted networks. To address this issue, we propose a novel centrality measure to identify the spreading ability of the Influential Spreaders using Potential Edge Weight (IS-PEW). Considering the connectivity structure, ability of information exchange, and the importance of neighboring nodes, we measure the potential edge weight. The ranking similarity of spreaders identified by the IS-PEW method and the baseline centrality methods is compared with Susceptible–Infectious–Recovered (SIR) epidemic simulator using Kendall’s rank correlation. Considering six different real networks, the spreading ability of the top-ranking spreaders is also compared for five different percentages of top-ranking node sets.
This model has been tested with Indian share market data (NSE sectoral in-dex data of 6 sectors) of 2015. Result shows it is possible to predict in short term (1 to 5 days in future) price movement of sectoral indices using other lagged correlated sector price index movement.