Tie Inducement using Closure Analysis in Information Networks

<p>This work addresses one important problem in Social Networks Analysis, namely link prediction. Link Prediction is important to understand and evaluate the change in structure for a certain social network over a given period of time. While different methods exist to address link prediction in this work we explore one such method, which is termed Closure Analysis. We work on two real social networks, Facebook and Wikipedia; while these two networks have very different properties the application of one link prediction method is different for each network. We study the application of this method on the two networks and examine the network change this brings over a period of time. This work also considers one influential factor, namely the most active people in the community.</p><p>Keywords: Artificial Intelligence, Social network, Information network, digraph, Closure Analysis, Connections, Influential node, network structure</p>

Name thesis:degree_name

MS

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Engineering and Applied Science: Computer Engineering

Grantor dc:publisher

University of Cincinnati

Year dc:date

2012

Author dc:creator

• Munimadugu, Hareendra

Contributors dc:contributor

• Ralescu, Anca

• Computer Engineering

Statement dc:rights

• unrestricted
• This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.

Language dc:language

English

Repository record dc:identifier

http://rave.ohiolink.edu/etdc/view?acc_num=ucin1353099669

OAI identifier oai:identifier

oai:etd.ohiolink.edu:ucin1353099669