[
    {
        "id": "osp-23160",
        "type": "article-journal",
        "title": "Just for FUNS: LLM-Guided Spatio-Temporal Graph Node Generation for Forecasting Unobserved Node States",
        "author": [
            {
                "family": "Li",
                "given": "Shuhao"
            },
            {
                "family": "Yang",
                "given": "Weidong"
            },
            {
                "family": "Liu",
                "given": "Changan"
            },
            {
                "family": "Zhuo",
                "given": "Wei"
            },
            {
                "family": "Zhou",
                "given": "Yingbo"
            },
            {
                "family": "Zhang",
                "given": "Fan"
            },
            {
                "family": "Luo",
                "given": "Siqiang"
            }
        ],
        "URL": "https://omanscience.com/en/articles/just-for-funs-llm-guided-spatio-temporal-graph-node-generation-for-forecasting-unobserved-node-states",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Spatio-temporal forecasting is a cornerstone of logistics, urban planning, and intelligent transportation systems. However, constrained by deployment costs and maintenance resources, sensor networks often lack comprehensive spatial coverage, rendering Forecast Unobserved Node States (FUNS) a critical yet formidable challenge. Conventional models rely on historical observations and typically falter when encountering nodes without prior records. To address this, we redefine the problem as a conditional generation task on spatio-temporal graphs and propose GenST, a framework that introduces Large Language Models (LLMs) as a semantic bridge, leveraging a pre-trained LLM fine-tuned to extract rich semantic features from node descriptions, such as functional zones and road network structures, to compensate for missing spatio-temporal signals. Specifically, we design a two-stage generative architecture: a Spatio-Temporal VAE first compresses spatio-temporal dynamics into a latent space, followed by a Generative Transformer (GenT) that reconstructs the future states of unobserved nodes from noise, guided by multi-modal conditions including semantics, geographic coordinates, and neighborhood contexts. Experiments on six traffic and two non-traffic datasets show GenST significantly outperforms existing baselines in zero-shot prediction tasks, demonstrating the practical potential of semantic-guided generation for mitigating spatio-temporal data sparsity."
    }
]