Community Detection within Social Networks
Community detection in networks has raised an important research topic in recent years. Finding a community in social network is to identify a set of nodes such that they interact with each other frequently than with those nodes outside the group. Also community detection can ease other social computing tasks that can be applied to several real world applications, community detection also can be used to compress large networks producing smaller ones, which make it much easier to visualize and understand.
The problem of detecting communities can be modeled as an optimization problem where a quality objective function that captures the intuition of a community as a set of nodes with better internal connectivity than external connectivity is selected to be optimized.
A lot of optimization methods have been used to solve the community detection problem. Over the previous years a lot of quality measures of communities have been introduced as objective functions in the optimization process such as the Modularity measure. Starting from this point of view we investigate some of the popular nature inspired optimization algorithms, in order to optimize different quality measures used widely in the literature.