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Permanent link (DOI): https://doi.org/10.7939/R3BS5S

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Ranking entities in heterogeneous multiple relation social networks using random walks Open Access

Descriptions

Other title
Subject/Keyword
ranking
social networks
random walks
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Sangi, Farzad
Supervisor and department
Osmar Zaiane (Computing Science)
Examining committee member and department
Jia You (Computing Science)
Dinesh Rathi (School of Library and Information Studies)
Department
Department of Computing Science
Specialization

Date accepted
2011-09-22T18:59:38Z
Graduation date
2011-11
Degree
Master of Science
Degree level
Master's
Abstract
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.
Language
English
DOI
doi:10.7939/R3BS5S
Rights
License granted by Farzad Sangi (fsangi@ualberta.ca) on 2011-09-16T02:09:31Z (GMT): Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. Where the thesis is converted to, or otherwise made available in digital form, the University of Alberta will advise potential users of the thesis of the above terms. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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