The papers cover a wide range of topics, such as deep/machine learning on graphs; probabilistic graphical models for biomedical data analysis; signal processing on graphs for biomedical image analysis; explainable AI (XAI) methods in geometric deep learning; big data analysis with graphs; graphs for small data sets; semantic graph research in medicine; modeling and applications of graph symmetry/equivariance; or graph generative models.