Ye Zhenqing, Hu Songnian, Yu Jun
James D. Watson Institute of Genome Sciences, Zhejiang University, Hangzhou, China.
Phys Rev E Stat Nonlin Soft Matter Phys. 2008 Oct;78(4 Pt 2):046115. doi: 10.1103/PhysRevE.78.046115. Epub 2008 Oct 30.
Community structure is common in various real-world networks; methods or algorithms for detecting such communities in complex networks have attracted great attention in recent years. We introduced a different adaptive clustering algorithm capable of extracting modules from complex networks with considerable accuracy and robustness. In this approach, each node in a network acts as an autonomous agent demonstrating flocking behavior where vertices always travel toward their preferable neighboring groups. An optimal modular structure can emerge from a collection of these active nodes during a self-organization process where vertices constantly regroup. In addition, we show that our algorithm appears advantageous over other competing methods (e.g., the Newman-fast algorithm) through intensive evaluation. The applications in three real-world networks demonstrate the superiority of our algorithm to find communities that are parallel with the appropriate organization in reality.
社区结构在各种现实世界网络中很常见；近年来，用于检测复杂网络中此类社区的方法或算法引起了极大关注。我们引入了一种不同的自适应聚类算法，该算法能够以相当高的准确性和鲁棒性从复杂网络中提取模块。在这种方法中，网络中的每个节点都充当一个自主代理，展示群体行为，即顶点总是朝着它们更喜欢的相邻组移动。在顶点不断重新分组的自组织过程中，这些活跃节点的集合可以形成一个最优的模块结构。此外，通过深入评估，我们表明我们的算法比其他竞争方法（例如纽曼快速算法）更具优势。在三个现实世界网络中的应用证明了我们的算法在寻找与现实中适当组织并行的社区方面的优越性。
