LLM-Enhanced Heterogeneous Graph Neural Networks for Research Project Conflict Detection

Wenjing Chang, Gege Qi, Yue Wang, Jianjun Yu

Abstract: Research project conflict risk detection aims to distinguish potential conflicts from multiple aspects including personnel allocation, resource distribution, and content overlap, etc. Existing research on project conflict detection faces three critical challenges: the difficulty in capturing semantic information from unstructured project documentation, limited model expressiveness in processing heterogeneous project relationships, and challenges in modeling diverse conflict patterns across interconnected project elements. To address these challenges, we first construct a research project heterogeneous graph with three types of nodes (i.e., projects, personnel, and resources) and five types of relationships capturing various project interactions. Besides, we propose a novel Semantic-augmented Heterogeneous grAph neural network for project conflict risk DEtection (SHADE) framework, equipped with three specially designed modules: 1) a semantic-augmented module leveraging large language models to extract fine-grained content representations from project descriptions, 2) a multi-view risk detection module with adaptive fusion to capture conflicts from diverse perspectives, and 3) a self-supervised contrastive learning module to enhance the discriminative power between positive and negative patterns. Extensive experiments on the real-world research project heterogeneous graph demonstrate that our proposed framework SHADE significantly outperforms state-of-the-art methods.

External IDs:dblp:conf/ijcnn/ChangQWY25