Alturki, Aseel Fahad (2022) Resilience, robustness and community structure of complex networks : applied to a real-world transport network. PhD thesis, University of Warwick.

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Abstract

In this research, we adopt a systems-of-systems approach by applying complex network analysis to transport networks, taking Englands’ railway network as a case study. Further, we adopt the notion that an understanding of a complex system’s higher order structures can be derived from an understanding of its components’ interactions. Furthermore, exploring the interplay between the structural and dynamical properties of the network is highly intriguing.

The general aim in the first study was to show how the interplay between the topological features of this (railway) infrastructure and the human mobility patterns occurring on the basis of these affects the performance of this particular transport network. We first measured the resilience and robustness of the network, drawing inspiration for our methods, in theoretical terms, from those used successfully with ecological networks. We show how topological resilience measures are strongly correlated to the performance of train operators in the network.

Furthermore, we conducted two community detection algorithm comparison studies. The general aim was to investigate which algorithms are producing the best community structure for this context. From our results, it emerged that Label Propagation and Leiden with directed modularity perform very well. Another objective of this research was to investigate the effects of incorporating edge weights (the numbers of travellers across an edge), as well as edge direction. We observed that taking weights and direction into account when detecting community structures might not necessarily have a noticeable impact in general. Therefore, we argue that in transport networks or networks that are similar to ours, the mechanism of the algorithm itself is more important in terms of the results being appropriate for the data and for the purposes behind the detection of the communities, and also for the type of communities required. More important, that is, than the pre-processing of the data.

Item Type:	Thesis (PhD)
Subjects:	H Social Sciences > HE Transportation and Communications Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > T Technology (General)
Library of Congress Subject Headings (LCSH):	Network analysis (Planning) -- Computer programs, Intelligent transportation systems, Railroads -- Joint use of facilities, Vehicular ad hoc networks (Computer networks)
Official Date:	January 2022
Dates:	Date Event January 2022 UNSPECIFIED
Institution:	University of Warwick
Theses Department:	Department of Computer Science
Thesis Type:	PhD
Publication Status:	Unpublished
Supervisor(s)/Advisor:	Jarvis, Stephen A., 1970- ; Guo, Weisi ; Leeke, Matthew (Researcher in computer science)
Sponsors:	Saudi Arabia. Wizārat al-Maʿārif ; Jāmiʿat al-Malik Saʿūd
Format of File:	pdf
Extent:	xxii, 149 leaves : illustrations, maps
Language:	eng

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