Research And Analysis On Link Prediction In Social Network

Social network is a specific form of a complex network.It refers to a network with self-organization,self-similarity and self-attraction.With technology advances,people need to deal with much more information than before,which provides a large amount of network data and also stimulates people's in-depth study of social networks.Link prediction meets this demand by providing a practical way to predict social networks.Link prediction is based on the current network topo construction to predict link.In theory,link prediction could simplify the complex network research,besides,evolution could also be identified based on the prediction.In practical applications,researcher could use user data set to make recommendation under link prediction.Therefore,as an important big data tool,link prediction has important scientific meaning in various fields.In this master's dissertation,we mainly conduce link prediction analysis on the YouTube user data set,detailed introduction to the existing link prediction methods,and propose an improved algorithm based on the Ada-Boost algorithm.In the traditional Ada-Boost algorithm,by means of weak classifier iteration,the post-weak classifier solves the errors of the pre-classifier,and finally forms a strong classifier.However,the traditional Ada-Boost algorithm fails to identify the situation of data noise,and in the case of data skew,there is no effective solution.Therefore,this dissertation proposes an improved Ada-Boost algorithm,which converts the error rate into positive and negative errors,and adjusts the weight to make the error-to-mean ratio in a fixed interval.It effectively solves the accuracy and stability problems caused by data skew.Based on a series of research,the paper concluded that improved Ada-Boost has better performance in precision and stability.In addition,this dissertation also introduces prediction methods of the weak classifier in the Ada-Boost algorithm.Prediction methods are mainly divided into two types:proximity prediction methods and centrality prediction methods.The former is mainly based on the local information,while the latter prefers to use global information to make predictions based on the network topology.