Subgraph Concept Networks: Concept Levels in Graph Classification

2026-04-20

AI Summary

Graph neural networks (GNNs) often suffer from semantic information loss during pooling operations in graph classification, which hinders their ability to provide interpretability at both subgraph and graph levels. To address this limitation, this work proposes the Subgraph Concept Network (SCN), which employs soft clustering of node concept embeddings to jointly and end-to-end distill semantic concepts at both subgraph and graph granularities. SCN is the first method to enable collaborative learning of multi-level concepts within GNNs, thereby overcoming the conventional reliance on node embeddings alone for interpretation. The approach achieves competitive graph classification performance while significantly enhancing model interpretability through explicit, hierarchical concept discovery.

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 reasoning process of Graph Neural Networks is complex and considered opaque, limiting trust in their predictions. To alleviate this issue, prior work has proposed concept-based explanations, extracted from clusters in the model's node embeddings. However, a limitation of concept-based explanations is that they only explain the node embedding space and are obscured by pooling in graph classification. To mitigate this issue and provide a deeper level of understanding, we propose the Subgraph Concept Network. The Subgraph Concept Network is the first graph neural network architecture that distils subgraph and graph-level concepts. It achieves this by performing soft clustering on node concept embeddings to derive subgraph and graph-level concepts. Our results show that the Subgraph Concept Network allows to obtain competitive model accuracy, while discovering meaningful concepts at different levels of the network.

Problem

• Graph Neural Networks
• Concept-based Explanations
• Graph Classification
• Subgraph Concepts
• Interpretability

Innovation

• Subgraph Concept Network
• concept-based explanation
• graph classification
• soft clustering
• hierarchical concepts