Ketsia Guichard-Sustowski

arXiv2026

Integrating Structure and Attributes for Transportation Network Partitioning via Optimal Transport

Transportation network partitioning is essential for applications such as traffic analysis, simulation, and mobility pattern identification. However, transportation networks combine structural information with heterogeneous operational attributes, ranging from scalar indicators to temporal profiles. Existing approaches generally rely on predefined formulations to integrate these sources of information, limiting the ability to control their relative influence. This paper proposes a flexible framework for partitioning heterogeneous transportation networks represented as attributed graphs. The proposed methodology relies on a distance-based graph representation and an optimal transport formulation based on the semi-relaxed Fused Gromov-Wasserstein discrepancy, enabling joint consideration of network structure and attributes with explicit control over their trade-off. The proposed methodology is evaluated on two transportation systems with distinct characteristics: an urban road network for traffic-oriented partitioning and a bicycle-sharing system for identifying usage-based communities. Results demonstrate the ability of the framework to adapt the resulting partitions according to different structural and attribute preferences.

stat.AP↗

arXiv2026

Optimal Transport-Based Clustering of Attributed Graphs with an Application to Road Traffic Data

In many real-world contexts, such as social or transport networks, data exhibit both structural connectivity and node-level attributes. For example, roads in a transport network can be characterized not only by their connectivity but also by traffic flow or speed profiles. Understanding such systems therefore requires jointly analyzing the network structure and node attributes, a challenge addressed by attributed graph partitioning, which clusters nodes according on both connectivity and attributes. In this work, we adapt transport-based approaches based on Gromov--Wasserstein (GW) discrepancy. We investigate how GW methods, traditionally used for general-purpose tasks such as graph matching, can be specifically adapted for node partitioning, an area that has been relatively underexplored. In the context of node-attributed graphs, we introduce an adaptation of the Fused GW method, offering theoretical guarantees and the ability to handle heterogeneous attribute types. Additionally, we propose to incorporate distance-based embeddings to enhance performance. The proposed approaches are systematically evaluated using a dedicated simulation framework and illustrated on a real-world transportation dataset. Experiments investigate the influence of target choice, assess robustness to noise, and provide practical guidance for attributed graph clustering. In the context of road networks, our results demonstrate that these methods can effectively leverage both structural and attribute information to reveal meaningful clusters, offering insights for improved network understanding.

stat.ME↗