Graphs are very flexible data structures that can be used to represent, at the same time, entities and the relationships among them. Graphs can describe networks of interacting elements, e.g. in social graphs or metabolomics, as well as data where topological variations influence the feature of interest, such as molecular compounds.
Data-driven processing and adaptive learning on structured data has a long-standing history but it has recently grown to become one of the most active research topics in the deep learning field.
This special session solicits contributions on the general topic of deep learning for graphs including wide themes such as graph representation learning, graph generation, interpretability of deep graph networks, learning for graphs as a means to integrate symbolic-subsymbolic information. We welcome works focusing on methodological advances, theoretical works, and also impacting applications of deep learning for graphs.