GraphSAGEnES: A Graph Neural Network Designed for Graphs with Low Feature Discriminability

Graph Neural Networks (GNNs) have emerged as powerful tools for learning representations of structured data. An essential element of these models is neighborhood aggregation, where a node’s representation is updated based on its context (neighbors). Some variants of GNNs consider only local neighbor information during node updating. Ignoring global structural details leads to inadequate learning and differentiation of graph structures. To address these challenges, we introduce GraphSAGEnES (GSnES), a new graph neural network which employs a different aggregation mechanism based on similarity and entropy. Empirical results conducted on the Stochastic Block Model, a random graph model with distinct vertex communities, demonstrate that GSnES proves to be an effective method in classification tasks—particularly in graphs with low feature separability—demonstrating statistically significant improved performance in terms of accuracy, balanced accuracy, F1 Score, and Matthews correlation coefficient. This analysis contributes to developing more efficient aggregation mechanisms, potentially improving GNN architectures for various applications. In addition to evaluating these performance indicators, the model quantitatively assesses the respective contributions of similarity and entropy to the outcome. In this sense, the aim is to mitigate one of the main limitations of GNN models, which is their lack of explainability.