X-節點：自我解釋即為所需

摘要

圖神經網絡（GNNs）通過捕捉數據實例間的結構依賴性，在計算機視覺和醫學影像分類任務中取得了頂尖的成果。然而，其決策過程仍大多不透明，這限制了其在需要高解釋性的關鍵臨床應用中的可信度。現有的GNN可解釋性技術通常為事後且全局性的，對單個節點決策或局部推理的洞察有限。我們提出了X-Node，這是一種自解釋的GNN框架，其中每個節點在預測過程中生成自己的解釋。對於每個節點，我們構建了一個結構化的上下文向量，編碼了其局部拓撲中的可解釋線索，如度數、中心性、聚類、特徵顯著性和標籤一致性。一個輕量級的推理模塊將此上下文映射為一個緊湊的解釋向量，該向量具有三個目的：(1) 通過解碼器重建節點的潛在嵌入以確保忠實性，(2) 使用預訓練的大型語言模型（如Grok或Gemini）生成自然語言解釋，以及(3) 通過“文本注入”機制將解釋反饋到消息傳遞管道中，從而引導GNN本身。我們在源自MedMNIST和MorphoMNIST的兩個圖數據集上評估了X-Node，並將其與GCN、GAT和GIN骨幹網絡集成。結果顯示，X-Node在保持競爭性分類精度的同時，能夠生成忠實的、針對每個節點的解釋。代碼庫：https://github.com/basiralab/X-Node。

English

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-節點：自我解釋即為所需

X-Node: Self-Explanation is All We Need

摘要

Support