DocumentCode
2757000
Title
Extending the classification of nodes in social networks
Author
Heatherly, Raymond ; Kantarcioglu, Murat
Author_Institution
Dept. of Comput. Sci., Univ. of Texas at Dallas, Dallas, TX, USA
fYear
2011
fDate
10-12 July 2011
Firstpage
77
Lastpage
82
Abstract
Because of computational concerns, social network analysis generally uses only directly connected nodes to perform classification tasks. However, recent research indicates that this method of classification may not consider that nodes in the graph could have different influence over other nodes near them in the graph. It is possible that well-selected nodes may have a stronger importance in a social graph. Here, we analyze methods by which these important nodes may be identified and used to improve the classification of nodes within the social graph. We also show the effect of incorporating these important nodes in social network classification.
Keywords
graph theory; pattern classification; social networking (online); directly connected nodes; graph nodes; node classification; social graph; social network analysis; Artificial neural networks; Convergence; Industries; Reflection; Social network services; Terrorism; Training;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligence and Security Informatics (ISI), 2011 IEEE International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4577-0082-8
Type
conf
DOI
10.1109/ISI.2011.5984054
Filename
5984054
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