Node attraction-facilitated evolution algorithm for community detection in networks

Node attraction-facilitated evolution algorithm for community detection in networks Network model recently has become a popular tool for studying complex systems. Detecting meaningful natural groups of nodes called communities in complex networks is an important task in network modeling and analysis. In this paper, the automatic network community detection is formulated as an optimization problem facilitated by node attraction. The basic idea is envision a network as a system of nodes where each node is attracted by its local neighbors. An evolution community detection algorithm is introduced, which employs a metric, named modularity Q as the fitness function and applies node attraction and modularity-based grouping crossover operator. The proposed algorithm faithfully captures the natural communities with high quality. Node attraction is easy to use for the speed up of the convergence of evolution algorithm to better partitions and for making the algorithm more stable. Node attraction does not require any threshold value. Experiments on synthetic and real-world networks further demonstrate the effectiveness of the proposed approach. Keywords Community detection · Complex networks · Evolutionary algorithms · Node attraction 1 Introduction (Strogatz 2001) and to study the structure and function of complex networks (Newman 2001) about 10 years ago. Networks are widely used for modeling many complex real- In http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Soft Computing Springer Journals

Node attraction-facilitated evolution algorithm for community detection in networks

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Publisher
Springer Journals
Copyright
Copyright © 2018 by Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics); Mathematical Logic and Foundations; Control, Robotics, Mechatronics
ISSN
1432-7643
eISSN
1433-7479
D.O.I.
10.1007/s00500-018-3267-x
Publisher site
See Article on Publisher Site

Abstract

Network model recently has become a popular tool for studying complex systems. Detecting meaningful natural groups of nodes called communities in complex networks is an important task in network modeling and analysis. In this paper, the automatic network community detection is formulated as an optimization problem facilitated by node attraction. The basic idea is envision a network as a system of nodes where each node is attracted by its local neighbors. An evolution community detection algorithm is introduced, which employs a metric, named modularity Q as the fitness function and applies node attraction and modularity-based grouping crossover operator. The proposed algorithm faithfully captures the natural communities with high quality. Node attraction is easy to use for the speed up of the convergence of evolution algorithm to better partitions and for making the algorithm more stable. Node attraction does not require any threshold value. Experiments on synthetic and real-world networks further demonstrate the effectiveness of the proposed approach. Keywords Community detection · Complex networks · Evolutionary algorithms · Node attraction 1 Introduction (Strogatz 2001) and to study the structure and function of complex networks (Newman 2001) about 10 years ago. Networks are widely used for modeling many complex real- In

Journal

Soft ComputingSpringer Journals

Published: Jun 6, 2018

References

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