Abstract: In recent years,with more and more attention drawning to link prediction in complex networks,and with the application of link prediction becoming increasingly extensive,a crucial question is raised on how to improve the accuracy of link prediction.Many proposals are made,among which the weighted similarity indices have already achieved a promising result.However,the traditional weighted network link prediction only considers the natural weight of the link neglects the influence of the topologi-cal weights on prediction accuracy.Therefore,aiming at the weighted networks,this paper takes the clustering and diffusion characteristics of edges into consideration and regard them as the topological weights of edges,and consequently recommended four similarity indices based on the topology weight of links,namely WCD-CN,WCD-AA,WCD-RA,and WCD-LP.This paper takes Matlab as the experimental platform and carries out experiments on two weighted datasets(USAir,Bibble) and two weightless datasets(Pblogs and Dolphins),in which AUC is used as the evaluation index.The results of the simulation indicate that compared with two weighted indices,which are based on natural weight and cluster coefficient respectively,the proposed algorithm has higher accuracy in prediction.

Key words: Complex network, Link prediction, Similarity index, Structural weight, Topological structure