Evaluating link significance in maintaining network connectivity based on link prediction

Evaluating the significance of nodes or links has always been an important issue in complex networks, and the definition of significance varies with different perspectives. The significance of nodes or links in maintaining the network connectivity is widely discussed due to its application in targeted attacks and immunization. In this paper, inspired by the weak tie phenomenon, we define the links’ significance by the dissimilarity of their endpoints. Some link prediction algorithms are introduced to define the dissimilarity of nodes based solely on the network topology. Experiments in synthetic and real networks demonstrate that the method is especially effective in the networks with higher clustering coefficients.Evaluating the significance of nodes or links has always been an important issue in complex networks, and the definition of significance varies with different perspectives. The significance of nodes or links in maintaining the network connectivity is widely discussed due to its application in targeted attacks and immunization. In this paper, inspired by the weak tie phenomenon, we define the links’ significance by the dissimilarity of their endpoints. Some link prediction algorithms are introduced to define the dissimilarity of nodes based solely on the network topology. Experiments in synthetic and real networks demonstrate that the method is especially effective in the networks with higher clustering coefficients.