Research
Concept Graph Convolutions: Message Passing in the Concept Space
arXiv:2604.20082v1 Announce Type: new Abstract: The trust in the predictions of Graph Neural Networks is limited by their opaque reasoning process. Prior methods have tried to explain graph networks v
arXiv:2604.20082v1 Announce Type: new Abstract: The trust in the predictions of Graph Neural Networks is limited by their opaque reasoning process. Prior methods have tried to explain graph networks via concept-based explanations extracted from the latent representations obtained after message passing. However, these explanations fall short of explaining the message passing process itself. To this aim, we propose the Concept Graph Convolution, the first graph convolution designed to operate on node-level concepts for improved interpretability. The proposed convolutional layer performs message passing on a combination of raw and concept representations using structural and attention-based edge weights. We also propose a pure variant of the convolution, only operating in the concept space. 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.
Related
- Subgraph Concept Networks: Concept Levels in Graph Classification
- Adversarial Robustness of Graph Transformers
- The Logical Expressiveness of Topological Neural Networks
- Sheaf Neural Networks on SPD Manifolds: Second-Order Geometric Representation Learning
- Topology-Aware PAC-Bayesian Generalization Analysis for Graph Neural Networks
Source: arXiv cs.LG | 2026-04-23