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Harnessing Large Language Models for Disaster Management: A Survey

https://doi.org/10.18653/v1/2025.findings-acl.750

Lei, Zhenyu; Dong, Yushun; Li, Weiyu; Ding, Rong; Wang, Qi R; Li, Jundong (January 2025, Association for Computational Linguistics)

Free, publicly-accessible full text available January 1, 2026
KG-CF: Knowledge Graph Completion with Context Filtering under the Guidance of Large Language Models

https://doi.org/10.1109/BigData62323.2024.10826107

Zheng, Zaiyi; Dong, Yushun; Wang, Song; Liu, Haochen; Wang, Qi; Li, Jundong (December 2024, IEEE)

Large Language Models (LLMs) have shown impressive performance in various tasks, including knowledge graph completion (KGC). However, current studies mostly apply LLMs to classification tasks, like identifying missing triplets, rather than ranking-based tasks, where the model ranks candidate entities based on plausibility. This focus limits the practical use of LLMs in KGC, as real-world applications prioritize highly plausible triplets. Additionally, while graph paths can help infer the existence of missing triplets and improve completion accuracy, they often contain redundant information. To address these issues, we propose KG-CF, a framework tailored for ranking-based KGC tasks. KG-CF leverages LLMs’ reasoning abilities to filter out irrelevant contexts, achieving superior results on real-world datasets.
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Free, publicly-accessible full text available December 15, 2025
Post-Disaster Private Well Water Contamination with Geosocial Network: A Case Study of Post-Hurricane Harvey

https://doi.org/10.1061/9780784485248.024

Ding, Rong; Dong, Yushun; Aldrich, Daniel P; Li, Jundong; Pieper, Kelsey; Ryan_Wang, Qi (January 2024, American Society of Civil Engineers)

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Abstract Graph Neural Networks (GNNs) have shown satisfying performance on various graph learning tasks. To achieve better fitting capability, most GNNs are with a large number of parameters, which makes these GNNs computationally expensive. Therefore, it is difficult to deploy them onto edge devices with scarce computational resources, e.g., mobile phones and wearable smart devices. Knowledge Distillation (KD) is a common solution to compress GNNs, where a light-weighted model (i.e., the student model) is encouraged to mimic the behavior of a computationally expensive GNN (i.e., the teacher GNN model). Nevertheless, most existing GNN-based KD methods lack fairness consideration. As a consequence, the student model usually inherits and even exaggerates the bias from the teacher GNN. To handle such a problem, we take initial steps towards fair knowledge distillation for GNNs. Specifically, we first formulate a novel problem of fair knowledge distillation for GNN-based teacher-student frameworks. Then we propose a principled framework named RELIANT to mitigate the bias exhibited by the student model. Notably the design of RELIANT is decoupled from any specific teacher and student model structures, and thus can be easily adapted to various GNN-based KD frameworks. We perform extensive experiments on multiple real-world datasets, which corroborates that RELIANT achieves less biased GNN knowledge distillation while maintaining high prediction utility. Open-source code can be found at https://github.com/yushundong/RELIANT.

Dong, Yushun; Zhang, Binichi; Yuan, Yiling; Zou, Na; Wang, Qi; Li, Jundong (January 2023, Proceedings of the 2023 SIAM International Conference on Data Mining (SDM))

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