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Volume 12, Issue 4
Temporal link prediction algorithm based on local random walk

YuanxiaoFan and Pei-ai Zhang

J. Info. Comput. Sci. , 12 (2017), pp. 255-263.

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  • 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.
  • Keywords

Strong and Weak solutions, temporal link prediction, Markov probability transfer matrix, local random walk.

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COPYRIGHT: © Global Science Press

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@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 = {2024}, 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 = {1746-7659}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/jics/22468.html} }
TY - JOUR T1 - Temporal link prediction algorithm based on local random walk AU - YuanxiaoFan and Pei-ai Zhang JO - Journal of Information and Computing Science VL - 4 SP - 255 EP - 263 PY - 2024 DA - 2024/01 SN - 12 DO - http://doi.org/ UR - https://global-sci.org/intro/article_detail/jics/22468.html KW - Strong and Weak solutions, temporal link prediction, Markov probability transfer matrix, local random walk. AB - 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.
YuanxiaoFan and Pei-ai Zhang. (2024). Temporal link prediction algorithm based on local random walk. Journal of Information and Computing Science. 12 (4). 255-263. doi:
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