fig2

Figure 2. Schematic of the message passing mechanism in GNNs. The input layer contains the original attributes. In the hidden layer, each node gathers information from its immediate neighbors and transforms it, creating an updated feature that fuses its own data with local context. The output layer repeats the aggregation, allowing every node to integrate signals from a wider neighborhood and produce a final, more informative representation. GNNs: Graph neural networks.