Degree
Master of Science in Data Science
Department
Department of Computer Science
Faculty/ School
School of Mathematics and Computer Science (SMCS)
Date of Submission
Fall 2022
Supervisor
Dr. Faraz Ahmed Zaidi, Assistant Professor, Department of Computer Science, School of Mathematics and Computer Science (SMCS)
Committee Member 1
Dr. Tahir Syed, Examiner – I, Institute of Business Administration (IBA), Karachi
Committee Member 2
Dr. Umair Azfar Khan, Examiner – II, Institute of Business Administration (IBA), Karachi
Keywords
Social Network Analysis, City Networks, Centrality Measures, Linear Threshold, Independent Cascade, Robustness
Abstract
Central nodes play a critical role in the structure and dynamics of complex networks. Identifying central nodes in these networks is a major issue that has attracted considerable attention in recent years. These nodes are useful to understand the behavior of networks, such as social networks, biological networks, communication, and transportation networks. There are several measures proposed in the literature to identify central nodes. As networks emerge from various fields ranging from biology (networks of protein interactions) to infrastructure (roads and railways), social networks (networks of friends) to the world wide web (internet), the available measures focus on domain specific features to identify central nodes in these networks. Furthermore, researchers use different methods to evaluate whether a node is central to a network. Determining the ideal measure for networks from different domains is an interesting problem given a wide variety of measures and evaluation methods exist in the literature. This research proposes to find central cities in networks formed as a result of economic ties between cities. Previous research with limited measures has shown that finding central cities in these networks is a challenging problem as not a single measure stands out as a front runner to detect centrality for the different evaluation criteria that exists in the literature. This thesis provides a comprehensive empirical analysis to study and compare numerous centrality measures. Results demonstrate that there isn't a single measure that performs consistently well on the city networks used for experimentation. We propose a new hybrid centrality measure that outperforms all the other network measures when evaluated using network robustness as a criterion.
Document Type
Restricted Access
Submission Type
Thesis
Recommended Citation
Ayesha, S. (2022). Identifying Central Nodes in Unweighted and Undirected Graphs – World City Networks (Unpublished graduate thesis). Institute of Business Administration, Pakistan. Retrieved from https://ir.iba.edu.pk/research-projects-msds/12
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