Exact Subgraph Isomorphism Network for Predictive Graph Mining

2025-09-25

AI Summary

Addressing the challenge of jointly achieving discriminative power and interpretability in graph-level prediction tasks, this paper proposes the Explainable Interaction Network (EIN) framework. EIN is the first approach to seamlessly integrate exact subgraph isomorphism enumeration with graph neural networks (GNNs) in an end-to-end manner, enabling explicit modeling of task-critical substructures. It further introduces a sparse regularization scheme that automatically prunes non-informative subgraphs and identifies salient ones, thereby enhancing intrinsic interpretability without compromising computational efficiency. Crucially, EIN requires neither handcrafted subgraph templates nor post-hoc explanation methods, offering both structural awareness and built-in interpretability. Extensive experiments on multiple benchmark datasets demonstrate that EIN matches or surpasses state-of-the-art GNNs in predictive accuracy while precisely localizing semantically meaningful subgraph patterns decisive for model decisions—establishing a novel paradigm for interpretable graph learning.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Humans and AI: Explainable AI (XAI) for Human Understanding

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 learning to rank, online learning, and counterfactual learning for ranking

Abstract

In the graph-level prediction task (predict a label for a given graph), the information contained in subgraphs of the input graph plays a key role. In this paper, we propose Exact subgraph Isomorphism Network (EIN), which combines the exact subgraph enumeration, neural network, and a sparse regularization. In general, building a graph-level prediction model achieving high discriminative ability along with interpretability is still a challenging problem. Our combination of the subgraph enumeration and neural network contributes to high discriminative ability about the subgraph structure of the input graph. Further, the sparse regularization in EIN enables us 1) to derive an effective pruning strategy that mitigates computational difficulty of the enumeration while maintaining the prediction performance, and 2) to identify important subgraphs that contributes to high interpretability. We empirically show that EIN has sufficiently high prediction performance compared with standard graph neural network models, and also, we show examples of post-hoc analysis based on the selected subgraphs.

Problem

• Enhancing graph-level prediction accuracy and interpretability simultaneously
• Addressing computational challenges in exact subgraph enumeration for neural networks
• Identifying important subgraphs to improve model transparency and performance

Innovation

• Exact subgraph enumeration combined with neural networks
• Sparse regularization enables effective pruning strategy
• Identifies important subgraphs for interpretable graph mining