Identifying important nodes with strong propagation capabilities in road
networks is a significant topic in the field of urban planning. However,
existing methods for evaluating nodes importance consider only topological
information and traffic volumes, ignoring the diversity of characteristics in
road networks, such as the number of lanes and average speed of road segments,
limiting their performance. To address this issue, this paper proposes a graph
learning-based node ranking method (MGL2Rank) that integrates the rich
characteristics of the road network. In this method, we first develop a
sampling algorithm (MGWalk) that utilizes multi-graph fusion to establish
association between road segments based on their attributes. Then, an embedding
module is proposed to learn latent representation for each road segment.
Finally, the obtained node representation is used to learn importance ranking
of road segments. We conduct simulation experiments on the regional road
network of Shenyang city and demonstrate the effectiveness of our proposed
method. The data and source code of MGL2Rank are available at
this https URL

本论文提出了一种基于图学习的节点排序方法 (MGL2Rank)，将道路网络的多样性特征集成在其中，包括道路路段的车道数和平均速度，实现了路网节点的重要性排序。我们采用多图融合的采样算法 (MGWalk) 来建立道路路段之间的关联，利用嵌入式模块学习每个路段的潜在表征，最终获得节点表征来学习道路路段的重要性。在实验中，我们以沈阳市的区域道路网络为例，证明了该方法的有效性。

基于多图融合的道路网络节点重要性排序学习

Learning to Rank the Importance of Nodes in Road Networks Based on  Multi-Graph Fusion

Random walks are ubiquitous in the sciences, and they are interesting from
both theoretical and practical perspectives. They are one of the most
fundamental types of stochastic processes; can be used to model numerous
phenomena, including diffusion, interactions, and opinions among humans and
animals; and can be used to extract information about important entities or
dense groups of entities in a network. Random walks have been studied for many
decades on both regular lattices and (especially in the last couple of decades)
on networks with a variety of structures. In the present article, we survey the
theory and applications of random walks on networks, restricting ourselves to
simple cases of single and non-adaptive random walkers. We distinguish three
main types of random walks: discrete-time random walks, node-centric
continuous-time random walks, and edge-centric continuous-time random walks. We
first briefly survey random walks on a line, and then we consider random walks
on various types of networks. We extensively discuss applications of random
walks, including ranking of nodes (e.g., PageRank), community detection,
respondent-driven sampling, and opinion models such as voter models.

本文综述了在网络上的随机游走的理论和应用，突出了单个和非自适应随机漫步器的简单情况。它探讨了随机游走的三种主要类型 —— 离散时间随机游走、节点中心的连续时间随机游走和边缘中心的连续时间随机游走，并深入讨论了一些应用，包括节点排名、社区检测、应答式抽样和选民模型等。