Finding and testing network communities by lumped Markov chains.
Finding and testing network communities by lumped Markov chains.
复制标题
DOI：
10.1371/journal.pone.0027028
复制
发表时间：
2011
期刊：
影响因子：
3.7
通讯作者：
Piccardi C
中科院分区：
文献类型：
--
作者：
Piccardi C
识别社区（或集群），即具有相对较强内部连通性的节点组，是深入理解网络结构和功能的基本任务。然而，缺乏界定社区和检验其重要性的正式标准。我们提出了一个尖锐的定义，是基于质量阈值。通过一个随机步行者的集总马尔可夫链模型，一个称为“持久性概率”的质量测量相关联的集群，然后定义为“社区”，如果这样的概率不小于。同样，由-个社区组成的划分是一个“-划分”。这些定义对于寻找和测试社区非常有效。如果一组候选分区可用，则设置所需的-级别允许立即选择具有最精细分解的-分区。同时，持久性概率量化了每个社区的质量。鉴于其单独评估每个集群的能力，这种方法也可以揭示单个定义明确的社区，即使在网络中，总体上不具有明确的集群结构。
Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition that is based on a quality threshold. By means of a lumped Markov chain model of a random walker, a quality measure called “persistence probability” is associated to a cluster, which is then defined as an “-community” if such a probability is not smaller than . Consistently, a partition composed of -communities is an “-partition.” These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired -level allows one to immediately select the -partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the quality of each single community. Given its ability in individually assessing each single cluster, this approach can also disclose single well-defined communities even in networks that overall do not possess a definite clusterized structure.
登录
查看更多内容
通过同源性生成的网络的聚类分析：自动鉴定参与癌症转移的重要蛋白质群落。
影响因子：
3
作者：
Jonsson, PF;Cavanna, T;Zicha, D;Bates, PA
通讯作者：
Bates, PA
DOI：
10.1088/1742-5468/2005/09/p09008
发表时间：
2005-09-01
影响因子：
2.4
作者：
Danon, L;Díaz-Guilera, A;Arenas, A
通讯作者：
Arenas, A
影响因子：
8.6
作者：
He, Jiankui;Deem, Michael W.
通讯作者：
Deem, Michael W.
影响因子：
2.4
作者：
Hu, Yanqing;Chen, Hongbin;Fan, Ying
通讯作者：
Fan, Ying
DOI：
10.1073/pnas.0605965104
发表时间：
2007-01-02
影响因子：
11.1
作者：
Fortunato, Santo;Barthelemy, Marc
通讯作者：
Barthelemy, Marc