Graph Neural Networks for Vehicular Social Networks: Trends, Challenges, and Opportunities

2025-11-18

AI Summary

This paper addresses two key challenges in vehicular social networks (VSNs): the difficulty of modeling complex traffic relationships and insufficient holistic graph-level integration. It systematically surveys recent advances in graph neural network (GNN) applications for VSNs, unifying the representation of road topology, trajectory flows, weather conditions, and other heterogeneous, non-Euclidean data sources across core tasks—traffic forecasting, signal control, and route planning. The work identifies a critical limitation: existing approaches predominantly operate on subgraphs, lacking end-to-end, full-graph modeling of the entire VSN and suffering from inadequate global graph representation. Through comprehensive analysis of state-of-the-art methods, benchmark datasets, and empirical performance, it demonstrates significant improvements in accuracy, robustness, and real-time capability enabled by GNNs. Finally, it proposes a full-graph learning framework for integrated VSNs and outlines future research directions, offering theoretical foundations and technical pathways for deep coordination and large-scale deployment of intelligent transportation systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Graph Neural Networks (GNNs) have emerged as powerful tools for modeling complex, interconnected data, making them particularly well suited for a wide range of Intelligent Transportation System (ITS) applications. This survey presents the first comprehensive review dedicated specifically to the use of GNNs within Vehicular Social Networks (VSNs). By leveraging both Euclidean and non-Euclidean transportation-related data, including traffic patterns, road users, and weather conditions, GNNs offer promising solutions for analyzing and enhancing VSN applications. The survey systematically categorizes and analyzes existing studies according to major VSN-related tasks, including traffic flow and trajectory prediction, traffic forecasting, signal control, driving assistance, routing problem, and connectivity management. It further provides quantitative insights and synthesizes key takeaways derived from the literature review. Additionally, the survey examines the available datasets and outlines open research directions needed to advance GNN-based VSN applications. The findings indicate that, although GNNs demonstrate strong potential for improving the accuracy, robustness, and real-time performances of on task-specific or sub-VSN graphs, there remains a notable absence of studies that model a complete, standalone VSN encompassing all functional components. With the increasing availability of data and continued progress in graph learning, GNNs are expected to play a central role in enabling future large-scale and fully integrated VSN applications.

Problem

• Surveying GNN applications in Vehicular Social Networks
• Analyzing VSN tasks like traffic prediction and routing
• Identifying research gaps in complete VSN modeling

Innovation

• GNNs model complex interconnected transportation data
• Leverage Euclidean and non-Euclidean traffic-related information
• Enhance VSN applications through systematic task categorization