OpenRank 动力学: 面向开源生态的影响力评估与动态传播模型

With the rapid development of the open source ecosystem, influence evaluation has become a critical tool for assessing developer contributions and project value. In open source communities, the complex heterogeneous network structures pose challenges for traditional static evaluation methods to comprehensively capture influence propagation among nodes. To address this issue, this paper proposes a OpenRank dynamic method that integrates static evaluation with dynamic propagation models to provide a multidimensional and dynamic assessment of node influence within open source communities. Firstly, the OpenRank algorithm is implemented using matrix algebra and the graph iteration method based on the Pregel framework, enabling efficient computation on both small- and large-scale networks and ensuring its scalability and adaptability. Secondly, by incorporating classic propagation models such as the Independent Cascade(IC) model, the Linear Threshold(LT) model, and the Susceptible-Infected- Recovered(SIR) model, this study analyzes influence propagation patterns, speed, and reach, addressing the limitations of traditional static evaluation methods. Experimental results demonstrate that the dynamic OpenRank method significantly outperforms traditional approaches in terms of influence propagation efficiency and reach. Additionally, it exhibits strong engineering adaptability and scalability.