Conformal Load Prediction with Transductive Graph Autoencoders

Graph machine learning has seen a surge in interest with the advent of complex networked systems in diverse domains. Applications include social and transportation networks and various kinds of biological systems. In most cases, the interaction between nodes is typically represented by edges with associated weights. The edge weights can embody varying characteristics, from the strength of interaction between two individuals in a social network to the traffic capacity of a route in a transportation system. The prediction of the edge weights is vital to understanding and modelling graph data. Graph Neural Networks (GNNs) have been successfully used on node classification and link prediction tasks.