  Combinatorial optimization is a well-established area in operations research
and computer science. Until recently, its methods have focused on solving
problem instances in isolation, ignoring that they often stem from related data
distributions in practice. However, recent years have seen a surge of interest
in using machine learning, especially graph neural networks (GNNs), as a key
building block for combinatorial tasks, either directly as solvers or by
enhancing exact solvers. The inductive bias of GNNs effectively encodes
combinatorial and relational input due to their invariance to permutations and
awareness of input sparsity. This paper presents a conceptual review of recent
key advancements in this emerging field, aiming at optimization and machine
learning researchers.
