A Multi-Feature Semantic Fusion and Bipartite Graph-Based Risk Identification Approach for Project Participation

Yue Wang, Yue Wang, Yujie Hu, Yujie Hu, Wenjing Chang, Wenjing Chang, Jianjun Yu

2024

Abstract

In the complex landscape of project management, ensuring the authenticity of participant involvement is paramount to achieving fairness, enforceability, and desired outcomes. Addressing the challenges posed by the heterogeneous nature of graphs, the underutilization of rich attribute information, and the scarcity of anomaly labels, we propose a Project Participation Authenticity Risk Identification Graph Neural Network (PARI-GNN), a novel architecture leveraging graph-based anomaly detection techniques to assess authenticity risks in project participation. PARI-GNN include a novel framework for risk identification using heterogeneous graphs. This method transforms heterogeneous graphs into bipartite graphs and combines multi-feature semantic fusion techniques with bipartite graph structures, providing a robust solution for identifying inauthentic participation. We evaluate our proposed model using real-world data. The experimental outcomes affirm the superior performance of PARI-GNN in accurately discerning authenticity risks, demonstrating the efficacy and competitive advantage of the proposed framework over a variety of state-of-the-art methodologies.

Paper Citation

in Harvard Style

Wang Y., Hu Y., Chang W. and Yu J. (2024). A Multi-Feature Semantic Fusion and Bipartite Graph-Based Risk Identification Approach for Project Participation. In Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS; ISBN 978-989-758-692-7, SciTePress, pages 907-914. DOI: 10.5220/0012742500003690

in Bibtex Style

@conference{iceis24,
author={Yue Wang and Yujie Hu and Wenjing Chang and Jianjun Yu},
title={A Multi-Feature Semantic Fusion and Bipartite Graph-Based Risk Identification Approach for Project Participation},
booktitle={Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS},
year={2024},
pages={907-914},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012742500003690},
isbn={978-989-758-692-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 26th International Conference on Enterprise Information Systems - Volume 1: ICEIS
TI - A Multi-Feature Semantic Fusion and Bipartite Graph-Based Risk Identification Approach for Project Participation
SN - 978-989-758-692-7
AU - Wang Y.
AU - Hu Y.
AU - Chang W.
AU - Yu J.
PY - 2024
SP - 907
EP - 914
DO - 10.5220/0012742500003690
PB - SciTePress