Abstract
A new perspective of dealing with link prediction problem was derived due to the application of mutual information in complex networks. Traditional mutual information algorithm (MI) not only considers the neighbor information of nodes, but also the structural information of common neighbors. Although MI has better performance compared with traditional methods which are based on common neighbors, it doesn’t effectively differentiate between different common neighbors. A new algorithm (MMI) was proposed by considering the influence of different common neighbors, which performs better than MI in precision.
Abstract
A new perspective of dealing with link prediction problem was derived due to the application of mutual information in complex networks. Traditional mutual information algorithm (MI) not only considers the neighbor information of nodes, but also the structural information of common neighbors. Although MI has better performance compared with traditional methods which are based on common neighbors, it doesn’t effectively differentiate between different common neighbors. A new algorithm (MMI) was proposed by considering the influence of different common neighbors, which performs better than MI in precision.