Ranking models to identify influential actors in large-scale social networks

Authors
Lu, Xiaohui
ORCID
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Other Contributors
Adali, Sibel
Goldberg, Mark
Kuruzovich, Jason N.
Magdon-Ismail, Malik
Issue Date
2013-08
Keywords
Computer science
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
Abstract
In this thesis, we develop a framework to identify prominent actors from several perspectives. We first investigate the importance of actors in actor-actor networks. In these networks, centrality algorithms are good candidates. However, these centrality measures suffer from several issues - they either look solely at the structure of the network disregarding issues like attention nodes have to give to others or make a shortest path interaction assumption that might be impractical in large networks. To address these issues, we develop two algorithms "Attentive Betweenness Centrality (ABC)" and "Attentive Closeness Centrality (ACC)". These two algorithms take multiple paths of information flow and attention into consideration while computing importance scores of actors. ABC reduces anomalous behaviors of classical betweenness centrality while captures its essence. ACC, on the other hand, targets the improvement of closeness. These two algorithms have high performance in identifying prominent actors.
Description
August 2013
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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