Title :
Link Analysis of Incomplete Relationship Networks
Author :
Harrington, Edward F.
Author_Institution :
Defence Sci. & Technol. Organ., Kingston, ACT
fDate :
March 1 2007-April 5 2007
Abstract :
We present a method of learning relationships at the triadic level of a relationship network. The method proposes learning linkages of a particular network using a support vector machine (SVM) classifier trained on the known part of a relationship network. Using features drawn from the topological information of the two degrees of separation of a link a classifier learns whether two people of that link are related or not. We investigate empirically the performance of the technique for various relationship networks derived from email, web hyperlinks, and questionnaires
Keywords :
learning (artificial intelligence); social sciences computing; support vector machines; incomplete relationship networks; learning relationships; link analysis; support vector machine classifier; topological information; Computational intelligence; Couplings; Data mining; Electronic mail; Recommender systems; Social network services; Support vector machine classification; Support vector machines; Viruses (medical); Web pages;
Conference_Titel :
Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0705-2
DOI :
10.1109/CIDM.2007.368844
