Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications

Ming Li, Alessio Micheli, Yu Guang Wang, Shirui Pan, Pietro Lió, Giorgio Stefano Gnecco, Marcello Sanguineti

Abstract: Deep neural networks for graphs (DNNGs) represent an emerging field that studies how the deep learning method can be generalized to graph-structured data. Since graphs are a powerful and flexible tool to represent complex information in the form of patterns and their relationships, ranging from molecules to protein-to-protein interaction networks, to social or transportation networks, or up to knowledge graphs, potentially modeling systems at very different scales, these methods have been exploited for many application domains.