Service-Oriented Computation for Insider Trading Detection in Multiplex Networked Industrial Chains

From the perspective of service computation, the current service-oriented architecture of industrial chain networks faces issues such as the lack of multiple computing services and insufficient detection capabilities, especially in the context of insider trading detection services for multiplex networked industrial chains. Existing service computation methods are often limited to single-market or simplified network structures, making it difficult to fully capture the dynamic changes and cross-chain propagation characteristics of insider trading within complex multi-layered industrial chain networks. Therefore, developing an efficient and accurate insider trading detection service computation method in the environment of multiplex networked industrial chains remains a critical research challenge. To address this, this paper proposes an Insider Trading Detection Service Computation Method for Multiplex Networked Industrial Chains based on Hybrid Temporal Granularity Scaling (ITDSC-MNICHTGS). This method combines insider trading feature modeling in multiplex networks, an adaptive time granularity adjustment mechanism, and unsupervised learning techniques to accurately identify insider trading behaviors in multiplex networked industrial chains without relying on data labeling. Experimental results show that, compared to traditional detection methods, the proposed method outperforms in metrics such as precision, recall, and $\mathbf{F 1}$-score, effectively improving the reliability and applicability of insider trading detection.