Editorial: Special Track on Graph Models for Learning and Recognition

Graphs, with their innate ability to encapsulate both topological and semantic information, have become a first-class tool in the landscape of pattern recognition and machine learning to represent weakly structured and heterogeneous data across countless domains. The field has evolved significantly since its early stages, with researchers pushing the boundaries of what graph-based learning algorithms can do. Inspired by the success of other deep learning models, various methods were introduced to redefine graph convolution, offering a suitable representation for spatio-temporal data as well. Graph-based representations find applications in diverse domains like computer vision, natural language processing, traffic forecasting, and molecular graph structure analysis in chemistry. This year's edition of the Graph Models for Learning and Recognition track is a testament to the relentless pursuit in understanding and harnessing the potential of these complex structures, thus reflecting the ever-expanding use of graph-based representations and models. The conference proceedings include a diverse range of papers, each addressing unique facets of graph-based representation, prediction, and model design, as well as related applications. We received high quality papers from all over the world, with a total of 15 submissions. The review process was very competitive, with each paper going through at least three reviews. As a result, 4 full papers were selected for presentation in the track. Three of them deal with enhancements in graph structure representation, in particular improving metapath-based processing on heterogeneous graphs and node classification, detecting change points in evolving graphs based on martingale theory, and exploring the impact of multiplicative integration-based layers for improved neighbourhood aggregation in GCNs. The fourth paper applies GCNs on 3D facial models for effective gender classification, showcasing results on the BP4D+ dataset. We would l