A Novel Functional Network Based on Three-way Decision for Link Prediction in Signed Social Networks

Aiming to reveal the potential relationships between users, link prediction has been considered as a fundamental research issue in signed social networks. The key of the link prediction is to measure the similarity between users. Many existing researches use connections between users and their common neighbors to measure the similarities, and these methods rely too much on the structure of social networks. Most of them use the deep neural network to enhance the prediction accuracy. However, the complete structure of the huge social network cannot be captured easily, and the models learnt by the deep neural network are unexplainable and uncontrolled. As an explainable model, functional network is a recent replacement for standard neural network. Therefore, we revise the traditional strategy of functional network and propose a novel functional network framework. Firstly, the attributes are preprocessed through the cloud model to define their importance before inputting them into the functional network. Then the association algorithm is used to do aggregate computation in computing neurons for defining the connections between neurons well. Finally, we use three-way decisions to process the samples in the boundary to optimize the performance of model. Experiments executed on six real datasets show that our method has significantly higher link prediction precision than the state-of-the-art works. From our discussions, the improved functional network can be a valid replacement for neural networks in some fields.