用主题模型思想让图生成过程可解释。
NGTM: Substructure-based Neural Graph Topic Model for Interpretable Graph Generation
- 将图视为潜在主题的混合，每个主题对应特定子结构分布。
- 生成时可精确控制结构特征，支持生物属性诱导。
- 适合需要理解生成逻辑的研究者，如药物设计领域。
图生成在分子设计和知识图谱构建等领域具有重要作用。尽管现有方法在生成真实图方面取得显著进展，但其可解释性仍受限，难以揭示结构决策背后的依据。为此，我们提出神经图主题模型（NGTM），一种受自然语言处理中主题建模启发的新型生成框架。NGTM将图表示为一组潜在主题的混合，每个主题定义了语义有意义子结构的分布，从而在局部和全局层面实现显式可解释性。生成过程通过整合主题分布与全局结构变量，使每个生成图的语义来源可追溯。实验表明，NGTM在生成质量上达到竞争力水平，同时实现了细粒度控制与可解释性，支持用户通过主题级调整来调控结构特性或诱导生物性质。
原文摘要 · Abstract (English)
Graph generation plays a pivotal role across numerous domains, including molecular design and knowledge graph construction. Although existing methods achieve considerable success in generating realistic graphs, their interpretability remains limited, often obscuring the rationale behind structural decisions. To address this challenge, we propose the Neural Graph Topic Model (NGTM), a novel generative framework inspired by topic modeling in natural language processing. NGTM represents graphs as mixtures of latent topics, each defining a distribution over semantically meaningful substructures, which facilitates explicit interpretability at both local and global scales. The generation process transparently integrates these topic distributions with a global structural variable, enabling clear semantic tracing of each generated graph. Experiments demonstrate that NGTM achieves competitive generation quality while uniquely enabling fine-grained control and interpretability, allowing users to tune structural features or induce biological properties through topic-level adjustments.
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