Many complex networks consist of a number of modules.
There are many complex networks available in real world as biological network, neural network, sports, hobbies, movies, religion, etc.
Many complex networks, despite their often-large size, have a relatively short average path length between any two vertices.
Our focus is on dynamical systems with uncountable state spaces, as many complex networks are of this type.
This is a common feature of many complex networks, closely related to the rich-gets-richer effect [4].
But, in many complex networks like the Internet there might not be one single resolution at which all the communities can be discovered.
We observe that most of the classical community detection methods fail to detect the Autonomous Systems as communities, mainly because the modular structure of the Internet (as that of many complex networks) is much richer than what can be captured by optimizing a global functional: Autonomous Systems have largely variable sizes, structures and functions.
Besides, one common feature of many complex networks, closely related to the rich-gets-richer effect [4], can also be observed in the SBBD community, as most authors have only few collaborations while there are few that correspond to high degree nodes.
Community structure is a universal and significant feature of many complex networks in biology, society, and economics.
The architecture and properties of many complex networks play a significant role in the functioning of the systems they describe.
