Leveraging Friendship Networks for Dynamic Link Prediction in Social Interaction Networks

TL;DR

This paper investigates how combining friendship and interaction networks in online social platforms can enhance the accuracy of predicting future interactions, demonstrating that current friendship data improves predictions.

Contribution

It introduces methods to integrate friendship networks into interaction link prediction algorithms and evaluates their effectiveness on Facebook data.

Findings

Incorporating current friendships improves prediction accuracy.

Predicted friendships do not significantly enhance predictions.

Combining networks yields better link prediction results.

Abstract

On-line social networks (OSNs) often contain many different types of relationships between users. When studying the structure of OSNs such as Facebook, two of the most commonly studied networks are friendship and interaction networks. The link prediction problem in friendship networks has been heavily studied. There has also been prior work on link prediction in interaction networks, independent of friendship networks. In this paper, we study the predictive power of combining friendship and interaction networks. We hypothesize that, by leveraging friendship networks, we can improve the accuracy of link prediction in interaction networks. We augment several interaction link prediction algorithms to incorporate friendships and predicted friendships. From experiments on Facebook data, we find that incorporating friendships into interaction link prediction algorithms results in higher accuracy, but incorporating predicted friendships does not when compared to incorporating current friendships.