A Discrete Krill Herd Optimization Algorithm For Community Detection

Citation:
Attia, K. A., A. I. Hafez, and A. ella hasanien, "A Discrete Krill Herd Optimization Algorithm For Community Detection", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.

Date Presented:

30 Dec

The rapid increase on the social networks presents
an urgent need for identifying the community detection. Community
detection is process of defining complex networks topology
or structure using an objective quality function by clustering or
grouping these complex networks as sets or groups of nodes and
edges based on their connectivity.
This paper presents a discrete Krill herd swarm optimization
algorithm for community detection problem (AKHSO) as
an efficient optimization technique to handle the problem of
complex networks community detection. AKHSO is able to
define dynamically the number of communities in the process.
A comparison is conducted with well-known community quality
measures and benchmarks. The experiment is executed on real
life popular benchmarks data sets. The experiment proved that
AKHSO can handle the community detection problem and define
the structure of complex networks with high accuracy and quality.