In this article, we aim to analyse the nature and epistemic consequences of what figures in network science as patterns of nodes and edges called 'communities'. Tracing these patterns as multi-faceted and ambivalent, we propose to describe the concept of community as a 'vague operator', a variant of Susan Leigh Star's notion of the boundary object, and propose that the ability to construct different modes of description that are both vague in some registers and hyper-precise in others, is core both to digital politics and the analysis of 'communities'. Engaging with these formations in terms drawn from mathematics and software studies enables a wider mapping of their formation. Disentangling different lineages in network science then allows us to contextualise the founding account of 'community' popularised by Michelle Girvan and Mark Newman in 2002. After studying one particular community detection algorithm, the widely-used 'Louvain algorithm', we comment on controversies arising with some of their more ambiguous applications. We argue that 'community' can act as a real abstraction with the power to reshape social relations such as producing echo chambers in social networking sites. To rework the epistemological terms of community detection and propose a reconsideration of vague operators, we draw on debates and propositions within the literature of network science to imagine a 'critical heuristics' that embraces partiality, epistemic humbleness, reflexivity and artificiality.

翻译：本文旨在分析网络科学中被称为"社区"的节点与边模式的性质及其认识论后果。通过追溯这些多面且矛盾的形态，我们提出将社区概念描述为"模糊算子"——这是苏珊·利·斯塔边界对象概念的变体，并指出构建在不同维度上既模糊又超精确的描述模式，既是数字政治的核心，也是分析"社区"的关键。运用数学与软件研究的概念框架审视这些形态，能够更全面地描绘其形成机制。厘清网络科学的不同谱系后，我们可以将米歇尔·吉万和马克·纽曼于2002年推广的"社区"基础论述置于历史语境中。在研究特定社区检测算法——广泛使用的"Louvain算法"——之后，我们评述了其某些模糊应用引发的争议。我们认为"社区"能够作为真实抽象物重塑社会关系，例如在社交网络中形成回声室效应。为重构社区检测的认识论框架并重新审视模糊算子，我们借鉴网络科学文献中的争论与主张，设想一种包含局部性、认识谦逊、反身性与人工性的"批判启发法"。