从模糊信息到社区检测：一种带有软信息的社会网络分析方法
From fuzzy information to community detection: an approach to social networks analysis with soft information
AI总结：
本文提出一种利用模糊集建模软信息并融合网络数据的新方法，用于社区检测，通过构建模糊测度和高效算法，在基准测试中取得高质量结果。
摘要：
在网络分析的基础上，以及在用模糊集对不精确或模糊信息进行建模的背景下，我们提出了一种创新的方式，将这些不确定知识分析、聚合并应用于现实问题的社区检测中。这项工作设定在一个或多个软信息源的存在下，这些信息源独立于所考虑的网络，假设这些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，某些人对特定法律的赞同程度，或者他们对几位政治家的立场。我们强调能够处理现实中通常出现的模糊性的重要性，因为语言术语的普遍使用。然后，我们提出了一种从模糊集构建模糊测度的建设性方法。这些测度是一种新的表示模型的基础，该模型将网络信息与模糊集信息相结合，特别是在涉及语言术语时。我们提出了该模型的一个具体应用，即在具有额外软信息的网络中寻找社区。为此，我们提出了一种高效算法，并通过基准测试过程衡量其性能，获得了高质量的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
