DocumentCode :
3498425
Title :
Supervised link prediction in weighted networks
Author :
De Sá, Hially Rodrigues ; Prudêncio, Ricardo B C
Author_Institution :
Center of Inf., Fed. Univ. of Pernambuco, Recife, Brazil
fYear :
2011
fDate :
July 31 2011-Aug. 5 2011
Firstpage :
2281
Lastpage :
2288
Abstract :
Link prediction is an important task in Social Network Analysis. This problem refers to predicting the emergence of future relationships between nodes in a social network. Our work focuses on a supervised machine learning strategy for link prediction. Here, the target attribute is a class label indicating the existence or absence of a link between a node pair. The predictor attributes are metrics computed from the network structure, describing the given pair. The majority of works for supervised prediction only considers unweighted networks. In this light, our aim is to investigate the relevance of using weights to improve supervised link prediction. Link weights express the `strength´ of relationships and could bring useful information for prediction. However, the relevance of weights for unsupervised strategies of link prediction was not always verified (in some cases, the performance was even harmed). Our preliminary results on supervised prediction on a co-authorship network revealed satisfactory results when weights were considered, which encourage us for further analysis.
Keywords :
learning (artificial intelligence); social networking (online); coauthorship network; social network analysis; supervised link prediction; supervised machine learning; weighted networks; Accuracy; Length measurement; Niobium; Prediction algorithms; Social network services; Weight measurement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks (IJCNN), The 2011 International Joint Conference on
Conference_Location :
San Jose, CA
ISSN :
2161-4393
Print_ISBN :
978-1-4244-9635-8
Type :
conf
DOI :
10.1109/IJCNN.2011.6033513
Filename :
6033513
Link To Document :
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