Comodularity and detection of co-communities
Comodularity and detection of co-communities
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DOI：
10.1103/physreve.104.054309
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发表时间：
2021-11-29
影响因子：
2.4
通讯作者：
Bartlett，Thomas E.
中科院分区：
文献类型：
--
作者：
Bartlett，Thomas E.
本文引入了共模性的概念，将二分网络的观测共聚为共社区。共聚类的任务是根据最相似的交互将一种类型的节点与另一种类型的节点分组在一起。共模性的措施被引入到评估的共同体的强度，以及安排表示的节点和集群的可视化，并定义一个目标函数进行优化。我们证明了我们提出的方法的有用性模拟数据，并从基因组学和消费品评论的例子。
This paper introduces the notion of comodularity, to cocluster observations of bipartite networks into co-communities. The task of coclustering is to group together nodes of one type with nodes of another type, according to the interactions that are the most similar. The measure of comodularity is introduced to assess the strength of co-communities, as well as to arrange the representation of nodes and clusters for visualization, and to define an objective function for optimization. We demonstrate the usefulness of our proposed methodology on simulated data, and with examples from genomics and consumer-product reviews.