Modular Structure of Complex Networks

• Author / Creator

  Rabbany khorasgani, Reihaneh

• Complex networks represent the relationships or interactions between entities in a complex system, such as biological interactions between proteins and genes, hyperlinks between web pages, co-authorships between research scholars. Although drawn from a wide range of domains, real-world networks exhibit similar structural properties and evolution patterns. A fundamental property of these networks is their tendency to organize according to an underlying modular structure, commonly referred to as clustering or community structure. This thesis focuses on comparing, quantifying, modeling, and utilizing this common structure in real-world networks. First, it presents generalizations of well-established traditional clustering criteria and propose proper adaptations to make them applicable in the context of networks. This includes generalizations and extensions of 1) the well-known clustering validity criteria that quantify the goodness of a single clustering; and 2) clustering agreement measures that compare two clusterings of the same dataset. The former introduces a new set of measures for quantifying the goodness of a candid community structure, while the latter establishes a new family of clustering distances suitable for comparing two possible community structures of a given network. These adapted measures are useful in both defining and evaluating the communities in networks. Second, it discusses generative network models and introduces an intuitive and flexible model for synthesizing modular networks that closely comply with the characteristics observed for real-world networks. This network synthesizer is particularly useful for generating benchmark datasets with built-in modular structure, which are used in evaluation of community detection algorithms. Lastly, it investigates how the modular structure of networks can be utilized in different contexts. In particular, it focuses on an e-learning case study, where the network modules can effectively outline the collaboration groups of students, as well as the topics of their discussions; which is used to monitor the participation trends of students throughout an online course. Then, it examines the interplay between the attributes of nodes and their memberships in modules, and present how this interplay can be leveraged for predicting (missing) attribute values; where alternative modular structures are derived, each in better alignment with a given attribute.

• Subjects / Keywords

  • Attributed Graphs
  • Clustering Agreement
  • Clustering Networks
  • Network Models
  • Community Detection
  • Complex Networks
  • Community Evaluation

• Graduation date

  Fall 2016

• Type of Item

  Thesis

• Degree

  Doctor of Philosophy

• DOI

  https://doi.org/10.7939/R3JQ0T55N

• License

  This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.