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.
| Date of Award | 31 Dec 2018 |
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| Original language | English |
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| Awarding Institution | - The University of Manchester
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| Supervisor | Nicholas Higham (Supervisor) & Stefan Guettel (Supervisor) |
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- Numerical linear algebra
- Numerical algorithms
- Knowledge graph
- Natural language processing
- Time-dependent networks
- Network sciences
- Evolving graphs
- Graph centrality
Evolving Graphs and Similarity-based Graphs with Applications
Zhang, W. (Author). 31 Dec 2018
Student thesis: Phd