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Title: Evolving graphs and similarity-based graphs with applications
Author: Zhang, Weijian
Awarding Body: University of Manchester
Current Institution: University of Manchester
Date of Award: 2018
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A graph is a mathematical structure for modelling the pairwise relations between objects. This thesis studies two types of graphs, namely, similarity-based graphs and evolving graphs. We look at ways to traverse an evolving graph. In particular, we examine the influence of temporal information on node centrality. In the process, we develop EvolvingGraphs.jl, a software package for analyzing time-dependent networks. We develop Etymo, a search system for discovering interesting research papers. Etymo utilizes both similarity-based graphs and evolving graphs to build a knowledge graph of research articles in order to help users to track the development of ideas. We construct content similarity-based graphs using the full text of research papers. And we extract key concepts from research papers and exploit the temporal information in research papers to construct a concepts evolving graph.
Supervisor: Higham, Nicholas ; Guettel, Stefan Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Numerical linear algebra ; Numerical algorithms ; Natural language processing ; Knowledge graph ; Time-dependent networks ; Network sciences ; Evolving graphs ; Graph centrality