Supervised Learning for Link Prediction in Weighted Networks

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 approach 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 approaches 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.