Tri-Stage Selective Reasoning for Rumor Source Detection via Graph Neural Networks and Large Language Models

Rumor source detection aims to identify the initial origin of misinformation diffusion in social networks. Accurate source localization is essential for effective rumor intervention and early mitigation in large-scale social media platforms. Existing rumor source detection methods often struggle to model complex propagation structures. However, applying mathematical models uniformly to all samples introduces unnecessary computational overhead and limits scalability. By leveraging GNN-based candidate ranking, our approach effectively narrows the source search space and provides a reliable structural foundation for subsequent reasoning. Prior studies typically perform end-to-end inference without considering prediction confidence, leading to inefficient processing of low-uncertainty samples. To address this issue, we introduce an entropy-based uncertainty filtering mechanism that selectively identifies high-uncertainty cases requiring further reasoning, significantly reducing redundant computation. Meanwhile, existing methods lack semantic interpretability when handling ambiguous propagation patterns, motivating the incorporation of large language model (LLM) reasoning. We employ LLM-based reasoning only on filtered samples to enhance semantic understanding while controlling inference cost. Based on these designs, we propose TSR-RSD, a tri-stage selective reasoning framework that integrates GNN-based structural modeling, uncertainty-driven sample selection, and LLM-based semantic reasoning. Experimental results on GossipCop, PolitiFact, and PHEME demonstrate that TSR-RSD consistently outperforms GNN-based baselines in terms of Hit@1, Hit@3, Hit@5, and Mean Reciprocal Rank (MRR), reflecting improved accuracy and stability in rumor source ranking. Furthermore, the entropy-based uncertainty filtering mechanism significantly reduces the LLM invocation ratio by approximately 40–60%, while maintaining comparable or improved ranking performance. As a result, TSR-RSD achieves an overall inference time reduction of 35–50%, effectively balancing localization accuracy, computational efficiency, and interpretability.