Degree
Master of Science in Computer Science
Department
Department of Computer Science
Faculty / School
School of Mathematics and Computer Science (SMCS)
Date of Submission
2021-08-15
Supervisor
Dr. Faraz Ahmed Zaidi, Assistant Professor, Department of Computer Science, School of Mathematics and Computer Science (SMCS)
Document type
MSCS Survey Report
Keywords
Network dismantling, Super spreaders, Giant component, Sub-graph, Influential nodes, Social network, Big networks, Dismantling techniques, Decycling, Vulnerable nodes, Network traversal, Comparative analysis
Abstract
The discovery and performance analysis of super spreaders is a particularly active field in Social Network Research. Numerous studies have been undertaken in order to ascertain the performance of these key nodes. In this project we developed and analyzed some of the network dismantling techniques and compared them to find the best technique that can be used for a variety of datasets and performs in adequate time limits. Our initiative will build on previous studies and, in some instances, use current research to develop and apply further network dismantling approaches, as well as study their efficacy and impact in large networks. The results that we obtained by running the benchmark with every technique on the same datasets have great similarity for majority of the algorithms showing that all algorithm identify the same influential nodes.
Recommended Citation
Shah, S. A. (2021). Developing a software to analyze and identify influential nodes in a network (Unpublished MSCS survey report). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/survey-reports-mscs/144
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