TY - CHAP
T1 - Exploratory factor analysis of graphical features for link prediction in social networks
AU - Madahali, Lale
AU - Najjar, Lotfi
AU - Hall, Margeret
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Social networks attract much attention due to their ability to replicate social interactions at scale. Link prediction, or the assessment of which unconnected nodes are likely to connect in the future, is an interesting but non-trivial research area. Three approaches exist to deal with the link-prediction problem: feature-based models, Bayesian probabilistic models, and probabilistic relational models. In feature-based methods, graphical features are extracted and used for classification. Usually, these features are subdivided into three feature groups based on their formula. Some formulas are extracted based on neighborhood graph traverse. Accordingly, there exist three groups of features: neighborhood features, path-based features, and node-based features. In this paper, we attempt to validate the underlying structure of topological features used in feature-based link prediction. The results of our analysis indicate differing results from the prevailing grouping of these features, which indicates that current literatures’ classification of feature groups should be redefined. Thus, the contribution of this work is exploring the factor loading of graphical features in link prediction in social networks. To the best of our knowledge, no prior studies had addressed it.
AB - Social networks attract much attention due to their ability to replicate social interactions at scale. Link prediction, or the assessment of which unconnected nodes are likely to connect in the future, is an interesting but non-trivial research area. Three approaches exist to deal with the link-prediction problem: feature-based models, Bayesian probabilistic models, and probabilistic relational models. In feature-based methods, graphical features are extracted and used for classification. Usually, these features are subdivided into three feature groups based on their formula. Some formulas are extracted based on neighborhood graph traverse. Accordingly, there exist three groups of features: neighborhood features, path-based features, and node-based features. In this paper, we attempt to validate the underlying structure of topological features used in feature-based link prediction. The results of our analysis indicate differing results from the prevailing grouping of these features, which indicates that current literatures’ classification of feature groups should be redefined. Thus, the contribution of this work is exploring the factor loading of graphical features in link prediction in social networks. To the best of our knowledge, no prior studies had addressed it.
KW - Data mining
KW - Exploratory factor analysis
KW - Social networks analysis
UR - http://www.scopus.com/inward/record.url?scp=85063196093&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-14459-3_2
DO - 10.1007/978-3-030-14459-3_2
M3 - Chapter
AN - SCOPUS:85063196093
T3 - Springer Proceedings in Complexity
SP - 17
EP - 31
BT - Springer Proceedings in Complexity
PB - Springer
ER -