Graph Machine Learning For Analyzing Complex Networks

This Special Issue focuses on the most recent advances in the models, algorithms, theories, and applications of Graph Machine Learning (GML), both in academic and industrial fields. Contributions to this Special Issue may include research on progress in the areas of graph classification, data augmentation, scalability, explainability, oversmoothing, heterophily, spectral methods, expressivity, temporal data, generative models, graph transformers, self-supervision, contrastive learning, adversarial attacks/ robustness, recommender systems, molecules, proteins, and other related theoretical and applied research.