An Approach to Cold-Start Link Prediction: Establishing Connections between Non-Topological and Topological Information

作者：Wang, Zhiqiang^*; Liang, Jiye; Li, Ru; Qian, Yuhua

来源：IEEE Transactions on Knowledge and Data Engineering, 2016, 28(11): 2857-2870.

DOI：10.1109/TKDE.2016.2597823

• Social network
• link prediction
• predictive model
• latent feature

Cold-start link prediction is a term for information starved link prediction where little or no topological information is present to guide the determination of whether links to a node will form. Due to the lack of topological information, traditional topology-based link prediction methods cannot be applied to solve the cold-start link prediction problem. Therefore, an effective approach is presented through establishing connections between non-topological and topological information. In the approach, topological information is first extracted by a latent-feature representation model, then a logistic model is proposed to establish the connections between topological and non-topological information, and finally the linking possibility between cold-start users and existing users is calculated. Experiments with three types of real-world social networks Weibo, Facebook, and Twitter show that the proposed approach is more effective in solving the cold-start link prediction problem and establishing connections between topological and non-topological information.

• 出版日期2016-11-1
• 单位山西大学