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, whose interaction with nodes of another type 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 illustrate the power of our proposed methodology on simulated data, as well as 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。共同集群的任务是将一种类型的节点组合在一起,这种节点与另一种类型的节点的相互作用最为相似。采用了新的共同模式衡量标准,以评估共同社区的力量,安排结点和组群的可视化代表,并界定优化的客观功能。我们举例说明了我们提议的模拟数据方法的力量,以及基因组学和消费品审查的实例。