Foundation and Generative Models for Graphs

The rapidly evolving field of machine learning for graphstructured data gathered significant attention due to its ability to preserve critical information inherent in complex data structures.As a result, significant efforts have been dedicated to designing advanced architectures and foundational models optimized for graph-based operations.Research in this area explores methodologies for graph representation learning and graph generation, incorporating probabilistic models such as variational autoencoders and normalizing flows.Despite increasing interest from researchers as well as their efforts in solving graph-related problems, several issues and areas remain to be addressed to improve model generalization and reliability.This tutorial reviews foundational concepts and challenges in graph representation, structure learning, and graph generation, while also summarizing the contributions accepted for publication in the special session on this topic at the