Temporal link prediction algorithm based on local random walk
Year: 2017
Journal of Information and Computing Science, Vol. 12 (2017), Iss. 4 : pp. 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.
Journal Article Details
Publisher Name: Global Science Press
Language: English
DOI: https://doi.org/2024-JICS-22468
Journal of Information and Computing Science, Vol. 12 (2017), Iss. 4 : pp. 255–263
Published online: 2017-01
AMS Subject Headings:
Copyright: COPYRIGHT: © Global Science Press
Pages: 9