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Community Mining: from Discovery to Evaluation and Visualization
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- Author / Creator
- Fagnan, Justin J
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Social networks are ubiquitous. They can be extracted from our purchase history
at on-line retailers, our cellphone bills, and even our health records. Mining tech-
niques that can accurately and efficiently identify interesting patterns in these net-
works are sought after by researchers from a variety of fields. The patterns they
seek often take the shape of communities, which are tightly-knit groups of nodes
that are more strongly related within the group than outside of the group.
This thesis proposes a series of algorithms that both accurately identify and
evaluate communities in social networks. In particular we show that relative valid-
ity criteria from the field of database clustering do not serve as adequate substitutes
in lieu of a ground truth. Furthermore we propose a novel community mining al-
gorithm that considers the number of internal and external triads within each com-
munity. Finally, we present two visualization algorithms that visually expose pre-
viously difficult to obtain information regarding the structure and relationships of
communities. We conclude this thesis with a brief summary of some open problems
in the area of community mining and visualization. -
- Graduation date
- Spring 2012
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- 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.