Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Shandilya, Anish; Gupta, Varuna; Alapatt, Bosco Paul; Poonia, Ramesh Chandra Title A name given to the resource VAST-GCN: An Attention-Driven Graph Convolutional Network (GCN) for Robust Cluster Head Selection in Vehicular Ad-Hoc Networks Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived International Journal of Networked and Distributed Computing;Volume;13;Issue;2;Article No.;25; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s44227-025-00069-w" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s44227-025-00069-w</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105019782239?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105019782239?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shandilya A., Christ University, Bengaluru, 560029, India; Gupta V., Christ University, Bengaluru, 560029, India; Alapatt B.P., Christ University, Bengaluru, 560029, India; Poonia R.C., Christ University, Bengaluru, 560029, India Description An account of the resource Vehicular Ad-Hoc Networks (VANETs) need smart and flexible communication protocols to deal with fast-moving vehicles and ever-changing network structures. Picking the right cluster head (CH) plays a key role to keep connections stable and reduce routing overhead. This paper presents VAST-GCN (Vehicular Attention-based Spatial-Temporal Graph Convolutional Network), a new model that uses attention to make vehicle grouping and CH selection better across different network sizes. VAST-GCN mixes Graph Convolutional Networks (GCNs) with Spatial, Temporal, and Channel Attention systems. Approach in vehicle settings with 100, 500, and 1000 vehicles has been tested using real-time info like speed and place. The design has transformer blocks to model time-based features and attention modules to improve space and feature relationships leading to better vehicle data. Data have been grouped using the K-Means method and checked with modularity score, silhouette score, and group density. At the time of comparison, it has been observed that VAST-GCN does better than regular GCN and MIXHOP GCN models in cutting down loss making better community structures, and keeping CHs stable when there are few vehicles or theyre moving fast. The proposed VAST-GCN framework exhibits clear advantages over existing spatio-temporal GNNs by delivering superior modularity, silhouette scores, and cluster head stability across diverse vehicular scenarios. Its attention-driven architecture not only improves clustering accuracy but also reduces packet delay and enhances throughput, highlighting its excellence as a robust and scalable solution for dynamic VANET environments. The Author(s) 2025. Subject The topic of the resource Cluster Head (CH) selection; Graph Convolutional Networks (GCNs); Network stability; Spatial-Temporal attention; Vehicle-to-Vehicle (V2V) communication; Vehicular Ad-Hoc Networks (VANETs) Publisher An entity responsible for making the resource available Springer Science and Business Media B.V. Relation A related resource ISSN: 22117938; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online