Robustness analysis of noise network link prediction

Link prediction is an important application in complex networks. It predicts existing but undiscovered associations or possible future relationships in the network. However, networks in real life have much noise. The networks we observe are incomplete or redundant which interfere with the effect of link prediction. This paper summarizes and constructs four kinds of common noises in social networks, then analyzes the robustness of traditional link prediction methods and methods based on network representation under the influence of different kinds and different degrees of noises on multiple social networks. The experimental results confirm that algorithms using local network properties have higher link accuracy, while methods based on the global properties have higher robustness. CCS CONCEPTS • Networks∼Network performance evaluation∼Network performance analysis • Networks∼Network performance evaluation∼Network experimentation • Networks∼Network performance evaluation∼Network performance modeling