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Predicting New Friendships in Social Networks

Predicting new links in social networks is an important problem within the network analysis literature. Many existing methods consider a single snapshot of the network as input, neglecting an important aspect of these networks, their temporal evolution over time. In this paper, we incorporate temporal information to solve the link prediction problem. Temporal information comes from the past interactions among the nodes in the network. Using a supervised learning framework, we train a binary classifier which predicts potential new links in the network in the next epoch. Anonymized Facebook data of New Orleans' users over a period of 28 months has been used in this study. Specifically, we train decision tree classifiers and feedforward neural networks to predict new relationships that are going to emerge in the underlying network. Our experiments clearly show that these models when trained with temporal information perform better compared to models trained with no temporal information.