PLUMS: Predicting Links Using Multiple Sources

Link prediction is an important problem in online social and collaboration networks, for recommending friends and future collaborators. Most of the existing approaches for link prediction are focused on building unsupervised or supervised classification models based on the availability of accepts and rejects of the past recommendations. Several of these methods are feature-based and they construct a large number of network-level features to make the prediction more effective. A more flexible approach is to allow the model to learn the required features from the network for a specific task, rather than explicit feature engineering. In addition, most of the social and collaboration relationships do not happen instantly and rather build slowly over time through several low cost interactions, such as email and chat. The existing approaches often ignore the availability of such auxiliary networks to make link prediction more robust and effective. The main focus of this work is to build a robust and effective classifier for link prediction using multiple auxiliary networks. We develop a supervised random walk model, that does not require any explicit feature construction, and can be personalized to each user based on the past accept and reject behavior. Our approach consistently outperforms several popular baselines in terms of precision and recall in multiple real-life data sets. Also, our approach is robust to noise and sparsity in auxiliary networks, while several popular baselines, specifically feature-based ones, are inconsistent in their performance under such conditions.