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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, 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 exists three groups of features, neighborhood features, path-based features, 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, there is no prior studies had addressed it.
Madahali L., Najjar L., Hall M. (2019) Exploratory Factor Analysis of Graphical Features for Link Prediction in Social Networks. In: Cornelius S., Granell Martorell C., Gómez-Gardeñes J., Gonçalves B. (eds) Complex Networks X. CompleNet 2019. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-14459-3_2