👉 The Message Passing (MsgP) algorithm, a core component of Graph Neural Networks (GNNs), is designed to enable information exchange and aggregation across nodes in a graph. It operates by iteratively updating each node's representation through two main steps: message passing and update. In the message passing step, each node collects messages from its neighbors—information that reflects the features and states of those neighbors—and aggregates this data to form a new representation. The update step then combines the aggregated messages with the node's current state using a learnable weight matrix, producing an updated representation. This process is repeated across multiple layers, allowing the network to capture complex dependencies and patterns within the graph structure. MsgP is particularly effective in tasks like node classification, link prediction, and graph classification, as it enables nodes to learn rich contextual information from their local neighborhood.
