Optimal Community Detection Approach based on Ant Lion Optimization

in
Citation:
Babers, R., N. I. Ghali, and A. E. Hassanien, "Optimal Community Detection Approach based on Ant Lion Optimization", IEEE iInternational Computer Engineering Conference - ICENCO , Cairo, 30 Dec, 2015.

Date Presented:

30 Dec

One of the important issue in Online Social Networks
(OSNs) in recent years is communities detection in
such networks. Social Networks depict the interactions between
individuals or entities and are represented by a graph of interconnected
nodes. The vast amount of data leads to the need of
analyzing such social network. Community detection problem can
be represented as an optimization problem as the objective is how
to divide the network to groups of nodes while the connectivity
between nodes in the same group is better than connectivity with
other nodes. In this research Ant Lion Optimization (ALO) has
been used as effective optimization method to detect the number
of communities in the networks automatically. The results show
that ALO succeed to find an optimization community structure
based on the quality function used.

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