Concept Graph Convolutions: Message Passing in the Concept Space

2026-04-21

AI Summary

This work addresses the limited transparency of graph neural network (GNN) inference by proposing a novel graph convolution approach that performs message passing within a node-level concept space, thereby enabling the first purely concept-space-based graph convolution. The method integrates raw input features with learnable concept representations and introduces a hybrid edge-weighting mechanism that combines structural priors with attention to enhance interpretability of concept evolution throughout the message-passing process. Experimental results demonstrate that the proposed approach achieves competitive task accuracy while significantly improving the understanding of GNNs’ internal decision-making logic.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

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.

Problem

• Graph Neural Networks
• interpretability
• concept-based explanations
• message passing
• reasoning process

Innovation

• Concept Graph Convolution
• Interpretable GNNs
• Message Passing
• Concept-based Explanation
• Attention-based Edge Weighting