Affiliation:
1. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, P. R. China
Abstract
Link prediction is an important issue for network evolution. For many real networks, future link prediction is the key to network development. Experience shows that improving reliability is an important trend of network evolution. Therefore, we consider it from a new perspective and propose a method for predicting new links of evolution networks. The proposed network reliability growth (NRG) model comprehensively considers the factors related to network structure, including the degree, neighbor nodes and distance. Our aim is to improve the reliability in link prediction. In experiments, we apply China high-speed railway network, China highway network and scale-free networks as examples. The results show that the proposed method has better prediction performance for different evaluation indexes. Compared with the other methods, such as CN, RA, PA, ACT, CT and NN, the proposed method has large growth rate and makes the reliability reach the maximum at first which save network construction resources, cost and improve efficiency. The proposed method tends to develop the network towards homogeneous network. In real networks, this structure with stronger stability is the goal of network construction. Therefore, our method is the best to improve network reliability quickly and effectively.
Funder
Fundamental Research Funds for the Central Universities
Publisher
World Scientific Pub Co Pte Lt
Subject
Computational Theory and Mathematics,Computer Science Applications,General Physics and Astronomy,Mathematical Physics,Statistical and Nonlinear Physics
Cited by
2 articles.
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1. Link Prediction in Social Networks using Vertex Entropy;International Journal of Recent Technology and Engineering (IJRTE);2023-07-30
2. A transportation network evolution model based on link prediction;International Journal of Modern Physics B;2021-11-03