The fast development of location-based social networks (LBSNs) has led to significant changes in society, resulting in popular studies of using LBSN data for socioeconomic prediction, e.g., regional population and commercial activity estimation. Existing studies design various graphs to model heterogeneous LBSN data, and further apply graph representation learning methods for socioeconomic prediction. However, these approaches heavily rely on heuristic ideas and expertise to extract task-relevant knowledge from diverse data, which may not be optimal for specific tasks. Additionally, they tend to overlook the inherent relationships between different indicators, limiting the prediction accuracy. Motivated by the remarkable abilities of large language models (LLMs) in commonsense reasoning, embedding, and multi-agent collaboration, in this work, we synergize LLM agents and knowledge graph for socioeconomic prediction. We first construct a location-based knowledge graph (LBKG) to integrate multi-sourced LBSN data. Then we leverage the reasoning power of LLM agent to identify relevant meta-paths in the LBKG for each type of socioeconomic prediction task, and design a semantic-guided attention module for knowledge fusion with meta-paths. Moreover, we introduce a cross-task communication mechanism to further enhance performance by enabling knowledge sharing across tasks at both LLM agent and KG levels. On the one hand, the LLM agents for different tasks collaborate to generate more diverse and comprehensive meta-paths. On the other hand, the embeddings from different tasks are adaptively merged for better socioeconomic prediction. Experiments on two datasets demonstrate the effectiveness of the synergistic design between LLM and KG, providing insights for information sharing across socioeconomic prediction tasks.

翻译：基于位置社交网络（LBSN）的快速发展已引发社会显著变革，促使利用LBSN数据进行社会经济预测的研究日益兴起，例如区域人口与商业活动估算。现有研究通过设计各类图结构以建模异构LBSN数据，并进一步应用图表征学习方法进行社会经济预测。然而，这些方法严重依赖启发式思路与专家经验从多元数据中提取任务相关知识，可能无法针对特定任务达到最优效果。此外，它们往往忽视不同指标间的内在关联，限制了预测精度。受大型语言模型（LLM）在常识推理、嵌入表示与多智能体协作方面卓越能力的启发，本研究创新性地融合LLM智能体与知识图谱进行社会经济预测。我们首先构建位置知识图谱（LBKG）以整合多源LBSN数据；进而利用LLM智能体的推理能力为每类社会经济预测任务识别LBKG中的相关元路径，并设计语义引导的注意力模块实现基于元路径的知识融合。此外，我们引入跨任务通信机制，通过在LLM智能体与知识图谱层面实现跨任务知识共享以进一步提升性能：一方面，不同任务对应的LLM智能体通过协作生成更丰富全面的元路径；另一方面，来自不同任务的嵌入表示通过自适应融合以优化社会经济预测。在两个数据集上的实验验证了LLM与知识图谱协同设计的有效性，为跨社会经济预测任务的信息共享提供了新思路。