Communities in social networks or graphs are sets of well-connected, overlapping vertices. Network community detection is a hot research topic in social, biological and information networks analysis. The effectiveness of a community detection algorithm is determined by accuracy in finding the ground-truth communities. In this article, we present two models to detect overlapping communities in large complex networks. In the first model, we introduce a parametrized notion of community, called k -linked community , that allows us to characterize a vertex/edge centered k -linked community with bounded diameter. Such community possesses a vertex/edge with a distance at most $$\frac{k}{2}$$ k 2 from any other vertex of that community. Next, we show how the problem of detecting vertex/edge centered k -linked communities can be expressed as a Partial Max-SAT optimization problem. Then, we propose a post-processing strategy to further enhance the overlaps between the final communities. Our second model called preference-based centroid model aims to constrain the choice of centroids communities in the first model. This new framework allows to integrate more easily the user preferences in order to discover high quality communities by selecting the most central vertices. For this, we exploit Weighted Partial Max-SAT to solve the underlying optimization problem. We evaluate the proposed frameworks empirically against several high-performing methods, with respect to three evaluation metrics and scalability, on a number of real-life datasets. The experimental results show that our algorithms outperform existing state-of-the-art methods in detecting relevant communities.

中文翻译：

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基于 SAT 的网络重叠社区检测模型

社交网络或图中的社区是一组连接良好、重叠的顶点。网络社区检测是社会、生物和信息网络分析中的一个热门研究课题。社区检测算法的有效性取决于找到真实社区的准确性。在本文中，我们提出了两种模型来检测大型复杂网络中的重叠社区。在第一个模型中，我们引入了社区的参数化概念，称为 k-linked community ，它允许我们表征具有有界直径的以顶点/边为中心的 k-linked 社区。这样的社区拥有一个顶点/边，与该社区的任何其他顶点的距离至多 $$\frac{k}{2}$$k 2 。下一个，我们展示了如何将检测以顶点/边为中心的 k 链接社区的问题表示为 Partial Max-SAT 优化问题。然后，我们提出了一种后处理策略，以进一步增强最终社区之间的重叠。我们的第二个模型称为基于偏好的质心模型，旨在限制第一个模型中质心社区的选择。这个新框架允许更容易地集成用户偏好，以便通过选择最中心的顶点来发现高质量的社区。为此，我们利用加权部分 Max-SAT 来解决底层优化问题。我们在许多现实生活数据集上针对三个评估指标和可扩展性，根据几种高性能方法对所提出的框架进行了经验评估。