Explainable Document Classification via Concept Whitening and Stable Graph Patterns

This paper proposes a novel explainable document classification framework that integrates Concept Whitening (CW) with graph concepts that are derived from stable graph patterns, and extracted via methods based on Formal Concept Analysis (FCA) and pattern structures. Document graphs are constructed using Abstract Meaning Representation (AMR) graphs, from which graph concepts are extracted and aligned with the latent space axes of Graph Neural Networks (GNNs) using CW. We investigate four types of graph concepts for their effect on concept alignment: frequent subgraphs, graph pattern concepts, filtered equivalence classes, and closed subgraphs. A novel filtration mechanism based on support, along with a custom penalty metric, is proposed to refine graph concepts for maximizing concept alignment. Experiments on the 10 Newsgroups and BBC Sport datasets show that our document graphs effectively capture both structural and semantic information, thereby supporting competitive classification performance across multiple GNN model architectures and configurations. For the 10 Newsgroups dataset, GNN models equipped with a CW module show an average increase of 0.7599 in the macro-averaged F1 score of the Concept Alignment Performance (CAP) metric, with an average drop of only 0.0025 in the document classification macro-averaged F1 score. Similarly, on the BBC Sport dataset, the average CAP improvement is 0.6998, with an average drop of 0.0894 in document classification performance. Additionally, concept gradient importance analyses and concept similarity heatmaps provide insights into the interpretability and structural separability of the GNN’s latent representations, achieved using CW.