Community detection is one of the most important methodological fields of network science, and one which has attracted a significant amount of attention over the past decades. This area deals with the automated division of a network into fundamental building blocks, with the objective of providing a summary of its large-scale structure. Despite its importance and widespread adoption, there is a noticeable gap between what is arguably the state-of-the-art and the methods that are actually used in practice in a variety of fields. Here we attempt to address this discrepancy by dividing existing methods according to whether they have a "descriptive" or an "inferential" goal. While descriptive methods find patterns in networks based on context-dependent notions of community structure, inferential methods articulate generative models, and attempt to fit them to data. In this way, they are able to provide insights into the mechanisms of network formation, and separate structure from randomness in a manner supported by statistical evidence. We review how employing descriptive methods with inferential aims is riddled with pitfalls and misleading answers, and thus should be in general avoided. We argue that inferential methods are more typically aligned with clearer scientific questions, yield more robust results, and should be in many cases preferred. We attempt to dispel some myths and half-truths often believed when community detection is employed in practice, in an effort to improve both the use of such methods as well as the interpretation of their results.

翻译：社区检测是网络科学中最重要的方法论领域之一，在过去几十年里吸引了广泛的关注。该领域涉及自动将网络划分为基本构建块，旨在提供其大规模结构的概要。尽管其重要性和广泛应用，但在被认为是当前最优的方法与各领域实际使用的技术之间存在明显差距。本文试图通过根据方法的目标是“描述性”还是“推断性”来划分现有方法，以解决这一差异。描述性方法基于上下文相关的社区结构概念在网络中寻找模式，而推断性方法则阐述生成模型并尝试将其拟合到数据中。通过这种方式，推断性方法能够揭示网络形成机制，并以统计证据支持的方式将结构与随机性分离。我们回顾了将描述性方法用于推断性目标如何充满陷阱和误导性答案，因此通常应避免这种做法。我们认为，推断性方法通常更符合清晰的科学问题，产生更稳健的结果，并且在许多情况下应优先选用。我们试图纠正实践中社区检测时常被相信的一些迷思和半真半假，以改进此类方法的使用及其结果的解释。