Insights are relative - influenced by a range of factors such as assumptions, scopes, or methods that together define a research perspective. In normative and empirical fields alike, this insight has led to the conclusion that no single perspective can generate complete knowledge. As a response, epistemological pluralism mandates that researchers consider multiple perspectives simultaneously to obtain a holistic understanding of their phenomenon under study. Translating this mandate to network science, our work introduces Network Pluralism as a conceptual framework that leverages multi-perspectivity to yield more complete, meaningful, and robust results. We develop and demonstrate the benefits of this approach via a hands-on analysis of complex legal systems, constructing a network space from references across documents from different branches of government as well as including organizational hierarchy above and fine-grained structure below the document level. Leveraging the resulting heterogeneity in a multi-network analysis, we show how complementing perspectives can help contextualize otherwise high-level findings, how contrasting several networks derived from the same data enables researchers to learn by difference, and how relating metrics to perspectives may increase the transparency and robustness of network-analytical results. To analyze a space of networks as perspectives, researchers need to map dimensions of variation in a given domain to network-modeling decisions and network-metric parameters. While this remains a challenging and inherently interdisciplinary task, our work acts as a blueprint to facilitate the broader adoption of Network Pluralism in domain-driven network research.

翻译：洞见是相对的——受假设、范围或方法等一系列共同定义研究视角的因素影响。无论在规范还是实证领域，这一洞见都导向一个结论：单一视角无法生成完整知识。作为回应，认识论多元主义要求研究者同时考虑多个视角，以获得对研究现象的整体理解。将这一要求引入网络科学，本文提出“网络多元主义”作为概念框架，利用多视角性产生更完整、有意义且稳健的结果。我们通过对复杂法律系统的实践分析，开发并展示了该方法的价值：构建一个网络空间，涵盖政府不同分支文档间的引用关系，并纳入文档层级之上的组织层级与之下的细粒度结构。利用多网络分析中的异构性，我们展示了互补视角如何帮助将高层发现置于具体情境中，对比源自同一数据的多个网络如何使研究者通过差异学习，以及将度量指标与视角关联如何提升网络分析结果的透明度与稳健性。为将网络空间作为视角进行分析，研究者需将特定领域的变化维度映射至网络建模决策与网络度量参数。尽管这仍是一项具有挑战性且本质跨学科的任务，我们的工作可作为蓝图，促进网络多元主义在领域驱动网络研究中的更广泛采用。