Predicting Social Links for New Users across Aligned Heterogeneous Social Networks

Online social networks have gained great success in recent years and many of them involve multiple kinds of nodes and complex relationships. Among these relationships, social links among users are of great importance. Many existing link prediction methods focus on predicting social links that will appear in the future among all users based upon a snapshot of the social network. In real-world social networks, many new users are joining in the service every day. Predicting links for new users are more important. Different from conventional link prediction problems, link prediction for new users are more challenging due to the following reasons: (1) differences in information distributions between new users and the existing active users (i.e., old users); (2) lack of information from the new users in the network. We propose a link prediction method called SCAN-PS (Supervised Cross Aligned Networks link prediction with Personalized Sampling), to solve the link prediction problem for new users with information transferred from both the existing active users in the target network and other source networks through aligned accounts. We proposed a within-target-network personalized sampling method to process the existing active users' information in order to accommodate the differences in information distributions before the intra-network knowledge transfer. SCAN-PS can also exploit information in other source networks, where the user accounts are aligned with the target network. In this way, SCAN-PS could solve the cold start problem when information of these new users is total absent in the target network.Comment: 11 pages, 10 figures, 4 table

Pseudo Cold Start Link Prediction with Multiple Sources in Social Networks

Link prediction is an important task in social networks and data mining for understanding the mechanisms by which the social networks form and evolve. In most link prediction researches, it is assumed either a snapshot of the social network or a social network with some missing links is available. Most existing researches therefore approach this problem by exploring the topological structure of the social network using only one source of information. However, in many application domains, in addition to the social network of interest, there are a number of auxiliary information available. In this work, we introduce the pseudo cold start link prediction with multiple sources as the problem of predicting the structure of a social network when only a small subgraph of the social network is known and multiple heterogeneous sources are available. We propose a two-phase supervised method: the first phase generates an efficient feature selection scheme to find the best feature from multiple sources thatisusedforpredictingthestructureinthesocialnetwork. In the second phase, we propose a regularization method to control the risk of over-fitting induced by the first phase. We assess our method empirically over a large data collection obtained from Youtube. The extensive experimental evaluations confirm the effectiveness of our approach.