The identification of important nodes with strong propagation capabilities in road networks is a vital topic in urban planning. Existing methods for evaluating the importance of nodes in traffic networks only consider topological information and traffic volumes, the diversity of the traffic characteristics in road networks, such as the number of lanes and average speed of road segments, is ignored, thus limiting their performance. To solve this problem, we propose a graph learning-based framework (MGL2Rank) that integrates the rich characteristics of road networks to rank the importance of nodes. This framework comprises an embedding module containing a sampling algorithm (MGWalk) and an encoder network to learn the latent representations for each road segment. MGWalk utilizes multi-graph fusion to capture the topology of road networks and establish associations between road segments based on their attributes. The obtained node representation is then used to learn the importance ranking of the road segments. Finally, a synthetic dataset is constructed for ranking tasks based on the regional road network of Shenyang City, and the ranking results on this dataset demonstrate the effectiveness of our method. The data and source code for MGL2Rank are available at .

中文翻译：

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MGL2Rank：基于多图融合学习对道路网络中节点的重要性进行排序

识别道路网络中具有强传播能力的重要节点是城市规划中的一个重要课题。现有的交通网络中节点重要性评估方法仅考虑拓扑信息和交通量，忽略了路网中车道数量、路段平均速度等交通特征的多样性，从而限制了其性能。为了解决这个问题，我们提出了一种基于图学习的框架（MGL2Rank），它集成了道路网络的丰富特征来对节点的重要性进行排序。该框架包括一个嵌入模块，其中包含采样算法（MGWalk）和一个编码器网络，用于学习每个路段的潜在表示。 MGWalk 利用多图融合来捕获道路网络的拓扑，并根据路段的属性建立路段之间的关联。然后使用获得的节点表示来学习路段的重要性排名。最后，基于沉阳市区域路网构建了用于排名任务的综合数据集，该数据集的排名结果证明了我们方法的有效性。 MGL2Rank 的数据和源代码可在 处获取。