HebCGNN: Hebbian-enabled causal classification integrating dynamic impact valuing

Abstract

Classifying graph-structured data presents significant challenges due to the diverse features of nodes and edges and their complex relationships. While Graph Neural Networks (GNNs) are widely used for graph prediction tasks, their performance is often hindered by these intricate dependencies. Leveraging causality holds potential in overcoming these challenges by identifying causal links among features, thus enhancing GNN classification performance. However, depending solely on adjacency matrices or attention mechanisms, as commonly studied in causal prediction research, is insufficient for capturing the complex interactions among features. To address these challenges, we present HebCGNN, a Hebbian-enabled Causal GNN classification model that incorporates dynamic impact valuing. Our method creates a robust framework that prioritizes causal elements in prediction tasks. Extensive experiments on seven publicly available datasets across diverse domains demonstrate that HebCGNN outperforms state-of-the-art models.