Abstract
Link prediction has attracted a lot of attention in recent years. While most researchers try to find effective prediction methods, they ignore using the key users for friendship expansion. In this paper, we study the important factors that affect people’s decision for building friendship, and quantify the relationship strength between users and their friends, based on which we build an overall hierarchical network. From the network, features are extracted to measure the closeness and difference between users, which are employed in supervised learning with classical classifiers for link prediction. Experimental results show that our proposed method substantially outperforms existing unsupervised link prediction methods in terms of AUROC (area under roc curve).
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Li, N., Feng, X., Ji, S., Xu, K. (2013). Modeling Relationship Strength for Link Prediction. In: Wang, G.A., Zheng, X., Chau, M., Chen, H. (eds) Intelligence and Security Informatics. PAISI 2013. Lecture Notes in Computer Science, vol 8039. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39693-9_7
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DOI: https://doi.org/10.1007/978-3-642-39693-9_7
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