The main difference between node classification tasks and traditional supervised classification tasks is that the nodes in a graph are not independent and identically distributed, which is usually assumed in the former case. Instead, we model an interconnected set of nodes, and leverage concepts such as homophily, structural equivalence, and heterophily. Node classification tasks are thus often referred to as semi-supervised.