Please use this identifier to cite or link to this item: https://idr.l3.nitk.ac.in/jspui/handle/123456789/7500
Title: A hybrid community detection based on evolutionary algorithms in social networks
Authors: Jami, V.
Ram Mohana Reddy, Guddeti
Issue Date: 2016
Citation: 2016 IEEE Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2016, 2016, Vol., , pp.-
Abstract: In social network analysis, community detection is an optimization problem of finding out partitions of maximum modularity density from a network. It is a NP-hard problem which can be done using evolutionary algorithms such as Particle Swarm Optimization, Cat Swarm Optimization, Genetic Algorithm and Genetic Algorithm with Simulated Annealing. In this work, we proposed an algorithm based on Genetic Algorithm with Simulated annealing for not being trapped into local optimal solution which is giving more better results. The main motto of our work is to get better communities with low computation cost. We tested our proposed algorithm on three standard datasets such as Zachary's Karate Club Dataset, American College Football and Dolphin Social Network Dataset. Experimental results demonstrate that our proposed algorithm outperforms state of the art approaches. � 2016 IEEE.
URI: https://idr.nitk.ac.in/jspui/handle/123456789/7500
Appears in Collections:2. Conference Papers

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