This paper introduces the notion of co-modularity, to co-cluster observations of bipartite networks into co-communities. The task of co-clustering is to group together nodes of one type with nodes of another type, according to the interactions that are the most similar. The novel measure of co-modularity is introduced to assess the strength of co-communities, as well as to arrange the representation of nodes and clusters for visualisation, and to define an objective function for optimisation. We demonstrate the usefulness of our proposed methodology on simulated data, and with examples from genomics and consumer-product reviews.
翻译:本文介绍共同模式的概念,将两边网络的共同集群观测引入共同社区。共同集群的任务是根据最相似的相互作用,将一种类型的节点和另一种类型的节点组合在一起。采用新颖的共同模式衡量方法是为了评估共同社区的力量,安排结点和组群的表述以便可视化,并界定优化的客观功能。我们展示了我们关于模拟数据的拟议方法以及基因组学和消费产品审查的实例的有用性。