@Article{JICS-12-255,
author = {YuanxiaoFan and Pei-ai Zhang},
title = {Temporal link prediction algorithm based on local random walk},
journal = {Journal of Information and Computing Science},
year = {2017},
volume = {12},
number = {4},
pages = {255--263},
abstract = { Link  prediction  is  an  important  part  of  complex  network  research.  Traditional  static  link 
prediction algorithm ignores that nodes and links in network are added and removed over time. But temporal 
link  prediction  can  use  the  information  of  historical  network  to  make  better  prediction.  Based  on  local 
random walk, this paper proposes a time-series random walk algorithm. Given link data for times 1 through T, 
then we predict the links at time T+1. The algorithm first computes the Markov probability transfer matrix at 
each time, then combines them into a transformation matrix, and applies the local random walk algorithm to 
obtain  the  final  prediction  result.  The  experimental  results  on  real  networks  show  that  our  algorithm 
demonstrates better than other algorithms. 
},
issn = {3080-180X},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22468.html}
}