Computer Science ›› 2020, Vol. 47 ›› Issue (5): 265-270.doi: 10.11896/jsjkx.190600031

• Computer Network • Previous Articles     Next Articles

Link Prediction Method Based on Weighted Network Topology Weight

YUAN Rong1, SONG Yu-rong1, MENG Fan-rong2   

  1. 1 College of Automation & College of Artificial Intelligence,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
    2 School of Computer,Network Space Security,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
  • Received:2019-06-06 Online:2020-05-15 Published:2020-05-19
  • About author:YUAN Rong,born in 1995,postgradua-te.Her main research interests include complex network and link prediction.
    SONG Yu-Rong,born in 1971,Ph.D,professor,is a member of China Computer Federation.Her main research interests include network information dissemination and its control.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China(61672298,61873326,61373136,61802155),Philosophy SocialScience Research Key Project Fund of Jiangsu University(G2018SJZDI142) and Social Sciences of Ministry of Education of China(17YJAZH071).

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, Topological structure, Link prediction, Similarity index, Structural weight

CLC Number: 

  • TP393.02
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