关键词: 复杂网络, 链路预测, 网络嵌入, 自动编码器, 城市路网

Abstract: Urban road network is a special complex network. The link prediction of road network has important application value in urban planning and urban structure evolution. As the road network is highly sparse and nonlinear, this paper proposes a road network link prediction model based on the Katz Auto Encoder Network Embedding（hereafter as KAENE）. This prediction model is a deep learning network embedding model based on the auto encoder with the following characteristics. First, it uses Katz similarity matrix to preserve the structural characteristics of the road network. Second, the multi-layer non-linear auto encoder is used to learn network characterization of the road network. Third, during the model training period, it uses LLEloss function to preserve the local characteristics of the road network. Forth, it also uses L2-norm to prevent overfitting. Finally, it combines with the directional characteristics of the road network to improve the link prediction accuracy of the road network. Through experiments, this paper compares the different performance between KAENE model and other link prediction models using different urban road network data, at the same time, it also identifies the influence of different embedded dimensions on the prediction accuracy of KAENE model. At last, the network characterization learning process of this model through visualization is known. The experimental results show that KAENE achieves a good performance in the link prediction tasks of six representative road network datasets at home and aboard.

Key words: complex networks, link prediction, network embedding, auto encoder, urban road network