Message Passing Graph Neural Network MPNN

Message Passing Neural Networks (MPNN) is a neural network framework for processing graph structured data. It was proposed by Gilmer et al. in 2017.Neural Message Passing for Quantum ChemistryThe core idea of MPNN is to update the representation of nodes in the graph through message passing and aggregation mechanisms, so that it can capture the local neighborhood information of the nodes.

The forward propagation process of MPNN is mainly divided into two stages: the message passing stage and the readout stage. In the message passing stage, each node collects information from its neighbor nodes, which is aggregated through a trainable message function. The node update function then combines the aggregated information with the current state of the node to update the feature representation of the node. This process is iterated until the predetermined number of iterations is reached or the stop condition is met. The readout stage summarizes the final states of all nodes through a readout function to obtain the global feature representation of the entire graph.

The MPNN framework has good versatility and flexibility, and it can cover a variety of graph neural network models, such as GCN, GAT, etc. In addition, the design concept of MPNN also provides a new perspective for the research of graph neural networks and promotes the application and development of graph neural networks in different fields.