Pabitra Mitra – författare

Thiswork presents link prediction similarity measures for social networks that exploitthe degree distribution of the networks. In the context of link prediction indense networks, the text proposes similarity measures based on Markov inequalitydegree thresholding (MIDTs), which only consider nodes whose degree is above a thresholdfor a possible link. Also presented are similarity measures based on cliques(CNC, AAC, RAC), which assign extra weight between nodes sharing a greater numberof cliques. Additionally, a locally adaptive (LA) similarity measure isproposed that assigns different weights to common nodes based on the degreedistribution of the local neighborhood and the degree distribution of thenetwork. In the context of link prediction in dense networks, the textintroduces a novel two-phase framework that adds edges to the sparse graph toforma boost graph.

This book comprises select peer-reviewed papers from the International Conference on IoT, Intelligent Computing and Security, IICS 2021. The contents focus on the latest research in artificial intelligence, IoT, intelligent computing, and leading technological convergence security challenges. The book also discusses AI-driven automation of highly connected smart devices across the globe presenting the fast technological shift with the futuristic scenario, bursting perspective of IoT, computational intelligence, and security concerns. This book supports the transfer of vital knowledge to the next generation of researchers, students, and practitioners in academia and industry.