Elsevier TDMErrica F.Micheli A.Podda M.Bacciu D.2025-06-162025-06-162020-09-010893-608010.1016/j.neunet.2020.06.00610.48550/arxiv.1912.12693https://ror.circle-u.eu/handle/123456789/633169The adaptive processing of graph data is a long-standing research topic which has been lately consolidated as a theme of major interest in the deep learning community. The snap increase in the amount and breadth of related research has come at the price of little systematization of knowledge and attention to earlier literature. This work is designed as a tutorial introduction to the field of deep learning for graphs. It favours a consistent and progressive introduction of the main concepts and architectural aspects over an exposition of the most recent literature, for which the reader is referred to available surveys. The paper takes a top-down view to the problem, introducing a generalized formulation of graph representation learning based on a local and iterative approach to structured information processing. It introduces the basic building blocks that can be combined to design novel and effective neural models for graphs. The methodological exposition is complemented by a discussion of interesting research challenges and applications in the field.OPENSocial and Information Networks (cs.SI)FOS: Computer and information sciencesComputer Science - Machine LearningKnowledge BasesComputer Science - Social and Information NetworksMachine Learning (stat.ML)Machine Learning (cs.LG)Deep LearningStatistics - Machine LearningDeep learning for graphs; Graph neural networks; Learning for structured datapublication02 engineering and technology0202 electrical engineering, electronic engineering, information engineeringdoi_dedup___32559609http://arxiv.org/abs/1912.1269311568/1045719