Message Passing Neural Networks (MPNNs) are a class of neural networks designed to operate on graph-structured data by iteratively updating node representations through message exchanges between neighboring nodes. This approach enables MPNNs to effectively capture complex relationships and dependencies within the graph, making them suitable for tasks like node classification, link prediction, and graph classification.