Graph Analysis

Graph analysis focuses on extracting meaningful information from the structure and relationships within data represented as graphs, with applications spanning diverse fields like social networks and bioinformatics. Current research emphasizes developing robust and scalable algorithms, including graph neural networks and adaptations of optimal transport methods, to analyze increasingly complex and large-scale graphs, as well as benchmarking these methods against human-level performance. This work is crucial for advancing our understanding of complex systems and enabling more effective data analysis across numerous scientific disciplines and practical applications, such as improving recommendation systems and detecting adversarial attacks on machine learning models. The development of new benchmarks and datasets is also a key focus to drive further progress in the field.