Ranking entities in heterogeneous multiple relation social networks using random walks

  • Author / Creator
    Sangi, Farzad
  • A Social Network or Information Network is a structure made up of nodes representing entities, and edges representing the relationships among nodes. Understanding the behaviour of social networks is known as Social Network Analysis (SNA). One of the most important applications of SNA is to find the similarity/relevance among entities in the network for a specific query. Finding the relevance between different entities, we are able to rank them based on each other. Ranking a set of entities with respect to one instance is required in many application domains. For example, in E-Advertisement, the goal is to show the most related advertisement to each user. This essentially means to rank the advertisements based on each user and to show the high ranked ones to the user. In this study we focus on ranking the entities in heterogeneous multiple relation social networks, networks for which nodes belong to different classes and relationships have different types.

  • Subjects / Keywords
  • Graduation date
  • Type of Item
  • Degree
    Master of Science
  • DOI
  • 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.
  • Language
  • Institution
    University of Alberta
  • Degree level
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Osmar Zaiane (Computing Science)
  • Examining committee members and their departments
    • Jia You (Computing Science)
    • Dinesh Rathi (School of Library and Information Studies)