  The 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.
