Uncovering latent structure in social networks using graph embeddings
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Authors
ALFaqeeh, Mosab
Date
Type
thesis
Language
eng
Keyword
graph embeddings , Comunity detection , Social network graph , Graph clustring
Alternative Title
Abstract
The data from real-world social networks is huge, heterogeneous, and most of the
attributes are unstructured. As a result, extensive investigation is necessary to transform
this data into a useful and informative format. Failing to process these unstructured
attributes results in a massive loss of potential. Unstructured attributes can
offer crucial additional context.
Identifying communities and clusters in social networks has been one of the commonly
studied problems of graph mining, and is recognized as a challenging necessary
task, and many open tasks are still poorly understood. We show that user information from social network platforms such as Instagram
can be clustered using similarities based independently on profiles, hashtags, images,
and explicit links. These similarity measures are converted to graphs and then embedded
in geometric spaces using spectral embedding techniques. Communities and
clusters in these geometric spaces correspond to groups of users with similar interests,
and such groups can be used for, for example, targeted marketing, content recommendations,
and creating a higher level of customer support.
We show in this thesis how to get the data, process attributes, and combine
structural information existing in the social network. We present a way to link the
different types of attributes with structural information to produce the full context of each user. Embedding techniques are employed to represent the subgraphs in a mutually consistent manner while preserving their entirety.
Our approach shows that similarity is better represented using attributes and
structural information together than by using any one of these independently, or
by merging them all. Our approach outperforms all of the conventional community
detection algorithms, often by a large margin. The clusters that are produced do
contain users with similar interests since topic models perform well on individual
clusters.
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Queen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canada
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.
ProQuest PhD and Master's Theses International Dissemination Agreement
Intellectual Property Guidelines at Queen's University
Copying and Preserving Your Thesis
This publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.