Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN
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 hetero...
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Zusammenfassung: | 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. |
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DOI: | 10.48550/arxiv.2411.00028 |