On the basis of network analysis, and within the context of modeling imprecision or vague information with fuzzy sets, we propose an innovative way to analyze, aggregate and apply this uncertain knowledge into community detection of real-life problems. This work is set on the existence of one (or multiple) soft information sources, independent of the network considered, assuming this extra knowledge is modeled by a vector of fuzzy sets (or a family of vectors). This information may represent, for example, how much some people agree with a specific law, or their position against several politicians. We emphasize the importance of being able to manage the vagueness which usually appears in real life because of the common use of linguistic terms. Then, we propose a constructive method to build fuzzy measures from fuzzy sets. These measures are the basis of a new representation model which combines the information of a network with that of fuzzy sets, specifically when it comes to linguistic terms. We propose a specific application of that model in terms of finding communities in a network with additional soft information. To do so, we propose an efficient algorithm and measure its performance by means of a benchmarking process, obtaining high-quality results.

翻译：在网络分析的基础上，结合利用模糊集对不精确或模糊信息进行建模的背景，我们提出了一种创新方法，用于分析、聚合这些不确定性知识并将其应用于现实问题中的社区检测。本研究基于一个（或多个）独立于所考虑网络的软信息源的存在，假设这种额外知识由模糊集向量（或向量族）建模。这些信息可能代表例如某些人同意某项特定法律的程度，或他们对多位政治人物的立场。我们强调处理现实生活因常用语言术语而普遍存在的模糊性的重要性。随后，我们提出了一种从模糊集构建模糊测度的建设性方法。这些测度构成了一种新表示模型的基础，该模型将网络信息与模糊集信息（尤其是涉及语言术语时）相结合。我们针对该模型提出了一种具体应用，即在具有额外软信息的网络中寻找社区。为此，我们设计了一种高效算法，并通过基准测试过程衡量其性能，获得了高质量的结果。