Networks community detection using artificial bee colony swarm optimization

Citation:
Hafez, A. I., Hossam M. Zawbaa, A. E. Hassanien, and A. A. Fahmy, "Networks community detection using artificial bee colony swarm optimization", The 5th International Conference on Innovations in Bio-Inspired Computing and Applications (Springer) IBICA2014, Ostrava, Czech Republic., 22-24 June, 2014.

Date Presented:

22-24 June

Abstract:

Community structure identification in complex networks has been an
important research topic in recent years. Community detection can be viewed as
an optimization problem in which an objective quality function that captures the
intuition of a community as a group of nodes with better internal connectivity
than external connectivity is chosen to be optimized. In this work Artificial bee
colony (ABC) optimization has been used as an effective optimization technique
to solve the community detection problem with the advantage that the number of
communities is automatically determined in the process. However, the algorithm
performance is influenced directly by the quality function used in the optimization
process. A comparison is conducted between different popular communities’
quality measures when used as an objective function within ABC. Experiments
on real life networks show the capability of the ABC to successfully find an optimized
community structure based on the quality function used.

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