Abstract:
Many types of social network are modelled as
graphs. Community detection has been an important research
area in social graph analysis. Community detection can be
viewed as an optimization problem. Nowadays, researchers use
nature-inspired algorithms to solve optimization problem.
Their goal is to find the optimal solution for a given problem.
In this paper, nature-inspired based artificial bee colony
algorithm with crossover and mutation is used to detect
community in social graphs. GraphX is built as a library on
the top of Spark by encoding graph as a collection of vertices
and edges. Comparative studies describe that the proposed
algorithm and other nature-inspired algorithms can effectively
detect the community structure on real world social graphs as
other traditional community detection algorithms.
