[目的/意义]零模型是网络分析中的重要工具，可用于控制潜在混杂因素，减少研究结论偏差。本文旨在提出高效的随机置换策略，以提升零模型在大规模网络分析中的可行性。[方法/过程]系统梳理零模型的基本原理与方法，讨论现有方法的优缺点，并针对引文网络中的常见约束（如考虑节点属性、优先连接性、无自环等），基于Fisher-Yates置换算法提出一系列高效的随机化算法。本文进一步优化置换策略，结合随机值优化算法和大根堆数据结构，在保证严格约束条件的同时将时间复杂度降至O(MlogM)。此外，通过图情领域期刊的引用偏好分析，对比不同零模型的研究结论差异，验证所提方法的有效性。[结果/结论]研究表明，零模型的选择需与研究问题匹配：简单模型易产生“稻草人”结论，而复杂模型可能导致过度拟合。不同零模型还可能导致结论差异，因此实际应用中需结合研究背景谨慎选择。本文讨论了常见的三种约束条件，未来可进一步探讨特殊约束以及不同约束的组合。
[Purpose/Significance] Null models are crucial tools in network analysis for controlling potential confounding factors and reducing bias in research conclusions. This study aims to propose efficient and scalable randomization strategies to improve the feasibility and accuracy of null models when applied to large-scale network analyses. [Method/Process] We systematically review the foundational principles and methodologies of null models, discussing their advantages and limitations and identifying appropriate research scenarios for their application. Building upon the Fisher-Yates reshuffle algorithm, we develop a suite of efficient randomization algorithms tailored to common constraints in citation networks, such as node attributes, preferential attachment, and the exclusion of self-loops. The reshuffle strategies are further optimized by integrating a random value optimization method and a max-heap data structure, which ensures strict adherence to constraints while reducing time complexity to O(Mlog M). Furthermore, through an empirical analysis of citation preferences among journals in the library and information science field, we compare outcomes generated by different null models to demonstrate the effectiveness of the proposed approaches. [Result/Conclusion] Our findings indicate that the selection of an appropriate null model must correspond to the specific research question. This work indicates that the choice of a null model must align with the research question(s): simple models may lead to “straw man” conclusions, while overly complex models risk overfitting. Moreover, different null models can produce varying results, emphasizing the necessity for cautious model selection grounded in the research context. This work discusses three scenarios of constraints; future research directions include the examination of specialized constraints and their combinations to broaden applicability.
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