摘要
为了能够较为准确的寻找大规模复杂网络中的社团结构,很多学者提出了寻找社团结构的算法。论文将三种不同的典型的群落算法用于免疫网络中,经过不同的算法比较,我们发现BC-Finder算法划分的群落跟我们的因子的通道生物功能群落较为贴近,而KT algorithm提出的快速算法得出的结果与我们的生物群落贴近的较差,而CH algorithm算法划分的结果介于它们两者之间,跟BC-Finder的结果较为接近。三种算法都各有其特点,KT algorithm的群落定义是基于网络的拓扑结构的,没有考虑网络中节点的功能因素,而CH algorithm和BC-Finder的群落定义不但考虑到了网络的拓扑也兼顾到网络的功能。同时我们也发现细胞分泌介质关系可能跟因子生物通道功能存在着某种关系。
In order to exactly find community in large complex network,many scholars have put forward algorithms to find community structure.This paper applies three community algorithms to immune network.By different community algorithms comparison,we find that the result of BC-Finder algorithm is similar to biology function community,and the result of KT algorithm community algorithm is different in a great degree from biology function community,and the result of CH algorithm is between BC-Finder and KT community.Also it reflects that there is a certain relationship between the cell secretion and cytokine pathway function.
出处
《电脑知识与技术》
2011年第6X期4432-4435,共4页
Computer Knowledge and Technology
关键词
复杂网络
群落算法
免疫网络
二分图
生物信息学
complex network
community algorithm
immune network
bipartite
bioinformatics
