关于模块网络的弱最优划分
On weakly optimal partitions in modular networks
摘要：
引入模块度作为对图中顶点集划分所导致的社区结构的一种度量。然后，它也成为一个目标函数，用于找到好的分区，具有很高的成功率。然而，一些工作已经显示出一个缩放限制和某些不稳定性时，发现社区与此标准。模块性已经被研究，提出了几种形式主义，如Potts模型中的Hamilton或谱划分中的Laplacian。在本文中，我们提出了一个新的概率形式主义来分析模块性，并从它推导出一个算法的基础上弱最优划分。该算法获得了高质量的分区，并可扩展到大型图。
英文摘要：
Modularity was introduced as a measure of goodness for the community structure induced by a partition of the set of vertices in a graph. Then, it also became an objective function used to find good partitions, with high success. Nevertheless, some works have shown a scaling limit and certain instabilities when finding communities with this criterion. Modularity has been studied proposing several formalisms, as hamiltonians in a Potts model or laplacians in spectral partitioning. In this paper we present a new probabilistic formalism to analyze modularity, and from it we derive an algorithm based on weakly optimal partitions. This algorithm obtains good quality partitions and also scales to large graphs.
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。
