X-Node: Self-Explanation is All We Need
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.
689f5494a4caabb4320e5f33
Prajit Sengupta
claimed_verified
2025-08-18T06:56:28.465Z
6789273bf3b11ab0bf61c45b
https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/FncqNw7-Ql5KaOtorqJoF.png
Prajit Sengupta
user
prajit123
689f5494a4caabb4320e5f34
Islem Rekik
X-Node: Self-Explanation is All We Need
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.
X-Node is a self-explaining GNN framework that generates per-node explanations by encoding local topology features and integrating them into the message-passing pipeline, maintaining accuracy while enhancing interpretability.
Graph neural networks GNNs computer vision medical image classification structural dependencies interpretability explainability X-Node structured context vector degree centrality clustering feature saliency label agreement Reasoner module explanation vector latent embedding decoder natural language explanation pre-trained LLM text-injection mechanism GCN GAT GIN MedMNIST MorphoMNIST