Most Graph Convolutions (GCs) proposed in the Graph Neural Networks (GNNs) literature share the principle of computing topologically enriched node representations based on the ones of their neighbors. In this paper, we propose a novel GNN named Tangent Graph Convolutional Network (TGCN) that, in addition to the traditional GC approach, exploits a novel GC that computes node embeddings based on the differences between the attributes of a vertex and the attributes of its neighbors. This allows the GC to characterize each node's neighbor by computing its tangent space representation with respect to the considered vertex.

Machine Learning (ML) achievements enabled automatic extraction of actionable information from data in a wide range of decisionmaking scenarios. This demands for improving both ML technical aspects (e.g., design and automation) and human-related metrics (e.g., fairness, robustness, privacy, and explainability), with performance guarantees at both levels. The aforementioned scenario posed three main challenges: (i) Learning from Complex Data (i.e., sequence, tree, and graph data), (ii) Learning Trustworthily, and (iii) Learning Automatically with Guarantees. The focus of this special session is on addressing one or more of these challenges with the final goal of Learning Trustworthily, Automatically, and with Guarantees from Complex Data.