While successful in many domains, traditional graph representations and standard algorithmic methods still fall short in capturing crucial higher-order properties of complex systems – from group collaborations to temporal dependencies. HONAI explores how the fusion of AI with advanced network models like simplicial complexes, manifolds, and hypergraphs can revolutionize our understanding of these systems.

Join us in exploring cutting-edge research at the intersection of higher-order network science and AI, focusing on network forecasting, prediction, and completion. We invite contributions from both network scientists and AI specialists, with a strong emphasis on research grounded in higher-order network analysis.