Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning

• URL: http://arxiv.org/abs/2604.11986v1
• Date: Mon, 13 Apr 2026 19:26:36 GMT
• Title: Exploring Concept Subspace for Self-explainable Text-Attributed Graph Learning
• Abstract summary: We introduce Graph Concept Bottleneck (GCB) as a new paradigm for self-explainable text-attributed graph learning.<n>GCB maps graphs into a subspace, concept bottleneck, where each concept is a meaningful phrase.<n>We empirically show that GCB achieves intrinsic interpretability with accuracy on par with black-box Graph Neural Networks.
• Score: 10.84756768352255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce Graph Concept Bottleneck (GCB) as a new paradigm for self-explainable text-attributed graph learning. GCB maps graphs into a subspace, concept bottleneck, where each concept is a meaningful phrase, and predictions are made based on the activation of these concepts. Unlike existing interpretable graph learning methods that primarily rely on subgraphs as explanations, the concept bottleneck provides a new form of interpretation. To refine the concept space, we apply the information bottleneck principle to focus on the most relevant concepts. This not only yields more concise and faithful explanations but also explicitly guides the model to "think" toward the correct decision. We empirically show that GCB achieves intrinsic interpretability with accuracy on par with black-box Graph Neural Networks. Moreover, it delivers better performance under distribution shifts and data perturbations, showing improved robustness and generalizability, benefitting from concept-guided prediction.

Related papers

• Concept Graph Convolutions: Message Passing in the Concept Space [14.872008669410098]
  We propose the first graph convolution designed to operate on node-level concepts for improved interpretability.<n>The proposed convolutional layer performs message passing on a combination of raw and concept representations using structural and attention-based edge weights.<n>Our results show that the Concept Graph Convolution allows to obtain competitive task accuracy, while enabling an increased insight into the evolution of concepts across convolutional steps.
  arXiv  Detail & Related papers  (2026-04-22T00:57:06Z)
• Subgraph Concept Networks: Concept Levels in Graph Classification [52.68412528211993]
  Subgraph Concept Network is first graph neural network architecture that distils subgraph and graph-level concepts.<n>It achieves this by performing soft clustering on node concept embeddings to derive subgraph and graph-level concepts.<n>Our results show that the Subgraph Concept Network allows to obtain competitive model accuracy, while discovering meaningful concepts at different levels of the network.
  arXiv  Detail & Related papers  (2026-04-20T21:44:05Z)
• Revealing Combinatorial Reasoning of GNNs via Graph Concept Bottleneck Layer [28.886850252681754]
  We develop a graph concept layer that can be integrated into any GNN architectures.<n>The predicted concept scores are projected to class labels by the selected discriminative layer.<n>It enforces the sparse reasoning of GNNs' predictions to fit the soft logical rule over graph concepts.
  arXiv  Detail & Related papers  (2026-03-02T16:07:24Z)
• FaCT: Faithful Concept Traces for Explaining Neural Network Decisions [56.796533084868884]
  Deep networks have shown remarkable performance across a wide range of tasks, yet getting a global concept-level understanding of how they function remains a key challenge.<n>We put emphasis on the faithfulness of concept-based explanations and propose a new model with model-inherent mechanistic concept-explanations.<n>Our concepts are shared across classes and, from any layer, their contribution to the logit and their input-visualization can be faithfully traced.
  arXiv  Detail & Related papers  (2025-10-29T13:35:46Z)
• Graph Concept Bottleneck Models [26.57626285653119]
  Concept Bottleneck Models (CBMs) provide explicit interpretations for deep neural networks through concepts.<n>We propose GraphCBMs: a new variant of CBM that facilitates concept relationships by constructing latent concept graphs.
  arXiv  Detail & Related papers  (2025-08-19T20:23:18Z)
• Foundations and Frontiers of Graph Learning Theory [81.39078977407719]
  Recent advancements in graph learning have revolutionized the way to understand and analyze data with complex structures. Graph Neural Networks (GNNs), i.e. neural network architectures designed for learning graph representations, have become a popular paradigm. This article provides a comprehensive summary of the theoretical foundations and breakthroughs concerning the approximation and learning behaviors intrinsic to prevalent graph learning models.
  arXiv  Detail & Related papers  (2024-07-03T14:07:41Z)
• Knowledge graphs for empirical concept retrieval [1.06378109904813]
  Concept-based explainable AI is promising as a tool to improve the understanding of complex models at the premises of a given user. Here, we present a workflow for user-driven data collection in both text and image domains. We test the retrieved concept datasets on two concept-based explainability methods, namely concept activation vectors (CAVs) and concept activation regions (CARs)
  arXiv  Detail & Related papers  (2024-04-10T13:47:22Z)
• Concept Gradient: Concept-based Interpretation Without Linear Assumption [77.96338722483226]
  Concept Activation Vector (CAV) relies on learning a linear relation between some latent representation of a given model and concepts. We proposed Concept Gradient (CG), extending concept-based interpretation beyond linear concept functions. We demonstrated CG outperforms CAV in both toy examples and real world datasets.
  arXiv  Detail & Related papers  (2022-08-31T17:06:46Z)
• Encoding Concepts in Graph Neural Networks [6.129235861306906]
  We introduce the Concept Module, the first differentiable concept-discovery approach for graph networks. The proposed approach makes graph networks explainable by design by first discovering graph concepts and then using these to solve the task. Our results demonstrate that this approach allows graph networks to attain model accuracy comparable with their equivalent vanilla versions.
  arXiv  Detail & Related papers  (2022-07-27T15:34:14Z)
• Towards Explanation for Unsupervised Graph-Level Representation Learning [108.31036962735911]
  Existing explanation methods focus on the supervised settings, eg, node classification and graph classification, while the explanation for unsupervised graph-level representation learning is still unexplored. In this paper, we advance the Information Bottleneck principle (IB) to tackle the proposed explanation problem for unsupervised graph representations, which leads to a novel principle, textitUnsupervised Subgraph Information Bottleneck (USIB) We also theoretically analyze the connection between graph representations and explanatory subgraphs on the label space, which reveals that the robustness of representations benefit the fidelity of explanatory subgraphs.
  arXiv  Detail & Related papers  (2022-05-20T02:50:15Z)
• Human-Centered Concept Explanations for Neural Networks [47.71169918421306]
  We introduce concept explanations including the class of Concept Activation Vectors (CAV) We then discuss approaches to automatically extract concepts, and approaches to address some of their caveats. Finally, we discuss some case studies that showcase the utility of such concept-based explanations in synthetic settings and real world applications.
  arXiv  Detail & Related papers  (2022-02-25T01:27:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.