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To understand the structure of a large-scale biological, social, or technological network, it can be helpful to decompose the network into smaller subunits or modules. In this article, we develop an information-theoretic foundation for the concept of modularity in networks. We identify the modules of which the network is composed by finding an optimal compression of its topology, capitalizing on regularities in its structure. We explain the advantages of this approach and illustrate them by partitioning a number of real-world and model networks. clustering compression information theory

Many objects in nature, from proteins to humans, interact in groups that compose social (1), technological (2), or biological systems (3). The groups form a distinct intermediate level between the microscopic and macroscopic descriptions of the system, and group structure may often be coupled to aspects of system function including …