Road network link prediction model based on subgraph pattern

Bin Wang, Xiaoxia Pan, Yilei Li, Jinfang Sheng, Jun Long, Ben Lu and Faiza Riaz Khawaja
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Bin Wang: School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China
Xiaoxia Pan: School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China
Yilei Li: School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China
Jinfang Sheng: School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China
Jun Long: School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China
Ben Lu: School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China
Faiza Riaz Khawaja: School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, P. R. China

International Journal of Modern Physics C (IJMPC), 2020, vol. 31, issue 06, 1-24

Abstract: Urban road network (referred to as the road network) is a complex and highly sparse network. Link prediction of the urban road network can reasonably predict urban structural changes and assist urban designers in decision-making. In this paper, a new link prediction model ASFC is proposed for the characteristics of the road network. The model first performs network embedding on the road network through road2vec algorithm, and then organically combines the subgraph pattern with the network embedding results and the Katz index together, and then we construct the all-order subgraph feature that includes low-order, medium-order and high-order subgraph features and finally to train the logistic regression classification model for road network link prediction. The experiment compares the performance of the ASFC model and other link prediction models in different countries and different types of urban road networks and the influence of changes in model parameters on prediction accuracy. The results show that ASFC performs well in terms of prediction accuracy and stability.

Keywords: Link prediction; subgraph pattern; network embedding; classification model; urban road network (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1142/S0129183120500837

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