Abstract
In literature of network science, a "community" refers to a group of nodes that are densely connected within the group but are sparsely connected with nodes outside the group. Community structure is a hallmark of a variety of social, biological and engineering networks. Especially in neuroscience, there have been growing interests in analyzing community structure of brain networks, which is expected to reveal functional modules in information processing in the brain. Community structure of brain networks is characterized by the following properties: Each community has some overlaps with others; communities are hierarchically organized. Most of algorithms proposed up to now fail to detect community structure with these properties. Here we put forward a method for detecting overlapping and hierarchical community stricture of networks. Community detection from C. elegans neuronal and macaque cortical networks by this method is examined.
