LAPS: A Lightweight Privilege-Allocation Prompting Framework for Source Localization

The widespread use of social-media graphs has provided a convenient channel for rumor propagation. Rapid localization of rumor sources is therefore crucial for mitigating diffusion and enabling punitive countermeasures. Source Localization (SL) aims to identify the origin nodes given partial infection observations. Although deep-learning-based SL approaches outperform traditional estimators, three fundamental limitations remain: (i) Model Complexity —existing methods enrich node embeddings with cascades of auxiliary features, yielding high-capacity but excessively complex representations, leading to an exponential increase in the number of model parameters; (ii) Annotation gap —to overcome the scarcity of real-world misinformation cascades, current pipelines repeatedly simulate diffusion from a fixed seed, eroding robustness on true, few-shot outbreaks; and (iii) Computational bottleneck —full-model retraining or recurrent cascade simulation is required for every new task, which disqualifies the solutions from real-time deployment. Inspired by the success of prompt learning in NLP and graph learning, we propose LAPS, a Lightweight privilege-Allocation Prompting framework for Source localization. LAPS first trims parameter explosion and data scarcity by pre-training a graph-level source region classifier on adaptive subgraphs with source-prior diffusion data. It then enables few-shot SL via a privilege-allocation prompt module that updates <1% of all the parameters, avoiding model retraining to facilitate efficiency. Extensive experiments on five real-world networks demonstrate the effectiveness and efficiency of our prompt-based framework on few-shot source localization task.