LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection
Shen, Lingzhi Long, Yunfei Cai, Xiaohao Chen, Guanming Wang, Yuhan Razzak, Imran Jameel, Shoaib
Shen, Lingzhi
Long, Yunfei
Cai, Xiaohao
Chen, Guanming
Wang, Yuhan
Razzak, Imran
Jameel, Shoaib
Supervisor
Department
Computational Biology
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
Organizational Units
Journal Issue
Abstract
Graph-based personality detection constructs graph structures from textual data, particularly social media posts. Current methods often struggle with sparse or noisy data and rely on static graphs, limiting their ability to capture dynamic changes between nodes and relationships. This paper introduces LL4G, a self-supervised framework leveraging large language models (LLMs) to optimize graph neural networks (GNNs). LLMs extract rich semantic features to generate node representations and to infer explicit and implicit relationships. The graph structure adaptively adds nodes and edges based on input data, continuously optimizing itself. The GNN then uses these optimized representations for joint training on node reconstruction, edge prediction, and contrastive learning tasks. This integration of semantic and structural information generates robust personality profiles. Experimental results on Kaggle and Pandora datasets show LL4G outperforms state-of-the-art models.
Citation
L. Shen et al., "LL4G: Self-Supervised Dynamic Optimization for Graph-Based Personality Detection," 2025 IEEE International Conference on Multimedia and Expo (ICME), Nantes, France, 2025, pp. 1-6, doi: 10.1109/ICME59968.2025.11209311.
Source
IEEE International Conference on Multimedia and Expo (ICME)
Conference
2025 IEEE International Conference on Multimedia and Expo (ICME)
Keywords
Personality Detection, Large Language Models, Graph Neural Networks, Self-Supervised Learning
Subjects
Source
2025 IEEE International Conference on Multimedia and Expo (ICME)
Publisher
IEEE