Musial-Gabrys, K. and Gao, F., 2016. Hybrid Link Prediction Model. In: The Sixth Workshop on Social Network Analysis in Applications (SNAA 2016) co-located with International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2016), 18-21 August 2016, San Francisco, USA.
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In network science several topology--based link prediction methods have been developed so far. The classic social network link prediction approach takes as an input a snapshot of a whole network. However, with human activities behind it, this social network keeps changing. In this paper, we consider link prediction problem as a time--series problem and propose a hybrid link prediction model that combines eight structure-based prediction methods and self-adapts the weights assigned to each included method. To test the model, we perform experiments on two real world networks with both sliding and growing window scenarios. The results show that our model outperforms other structure--based methods when both precision and recall of the prediction results are considered.
|Item Type:||Conference or Workshop Item (Paper)|
|Group:||Faculty of Science & Technology|
|Deposited By:||Unnamed user with email symplectic@symplectic|
|Deposited On:||07 Sep 2016 13:07|
|Last Modified:||07 Sep 2016 13:07|
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