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Identifying important nodes with strong propagation capabilities in roadnetworks is a significant topic in the field of urban planning. However,existing methods for evaluating the importance of nodes in traffic networkconsider only topological information and traffic volumes, ignoring thediversity of characteristics in road networks, such as the number of lanes andaverage speed of road segments, limiting their performance. To solve thisproblem, we propose a graph learning-based framework (MGL2Rank) that integratesthe rich characteristics of road network for ranking the importance of nodes.In this framework, we first develop an embedding module that contains asampling algorithm (MGWalk) and an encoder network to learn latentrepresentation for each road segment. MGWalk utilizes multi-graph fusion tocapture the topology of the road network and establish associations among roadsegments based on their attributes. Then, we use the obtained noderepresentation to learn the importance ranking of road segments. Finally, weconstruct a synthetic dataset for ranking tasks based on the regional roadnetwork of Shenyang city, and our ranking results on this dataset demonstratethe effectiveness of our proposed method. The data and source code of MGL2Rankare available at https://github.com/ZJ726.

MGL2Rank: Learning to Rank the Importance of Nodes in Road Networks Based on Multi-Graph Fusion