Establishing group structure in complex networks is potentially very useful since nodes belonging to the same module can often be related by commonalities in their biological function. However, module detection in complex networks poses a challenging problem and has sparked a great deal of interest in various disciplines in recent years [5]. In real networks, which can be quite complex, we have no idea about the true number of modules that exist. Furthermore, the structure of the modules
may be hierarchical meaning they may be further divided into sub-modules and so forth. Many attempts have been made to deal with these problems and because the involved methods vary considerably they have been difficult to compare [5]. The objectives of this thesis are (i) to create and implement a new algorithm that will
identify modules in complex networks and reconstruct the network in such a way so as to maximize modularity, (ii) to evaluate the performance of a new method, and compare it to a popular method based on a simulated annealing algorithm, and
(iii) to apply the new method, and a comparator method, to analyze the metabolic
network of the bacterial genus Listeria, an important pathogen in both agricultural
and human clinical settings.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:NSHD.ca#10222/13319 |
| Date | 30 March 2011 |
| Creators | Urquhart, Caroline |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
Page generated in 0.0018 seconds