X-Node: Self-Explanation is All We Need
Authors: name: Prajit Sengupta, name: Islem Rekik

Graph neural networks (GNNs) have achieved state-of-the-art results in
computer vision and medical image classification tasks by capturing structural
dependencies across data instances. However, their decision-making remains
largely opaque, limiting their trustworthiness in high-stakes clinical
applications where interpretability is essential. Existing explainability
techniques for GNNs are typically post-hoc and global, offering limited insight
into individual node decisions or local reasoning. We introduce X-Node, a
self-explaining GNN framework in which each node generates its own explanation
as part of the prediction process. For every node, we construct a structured
context vector encoding interpretable cues such as degree, centrality,
clustering, feature saliency, and label agreement within its local topology. A
lightweight Reasoner module maps this context into a compact explanation
vector, which serves three purposes: (1) reconstructing the node's latent
embedding via a decoder to enforce faithfulness, (2) generating a natural
language explanation using a pre-trained LLM (e.g., Grok or Gemini), and (3)
guiding the GNN itself via a "text-injection" mechanism that feeds explanations
back into the message-passing pipeline. We evaluate X-Node on two graph
datasets derived from MedMNIST and MorphoMNIST, integrating it with GCN, GAT,
and GIN backbones. Our results show that X-Node maintains competitive
classification accuracy while producing faithful, per-node explanations.
Repository: https://github.com/basiralab/X-Node.