Community detection based on modularity and an improved genetic algorithm

Shang, Ronghua; Bai, Jing; Jiao, Licheng; Jin, Chao

doi:10.1016/j.physa.2012.11.003

Cited by 179 publications

(78 citation statements)

References 31 publications

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“…On the basis of references [9][10][11][12][13][14][15] and analysis of them, this paper gives the parameter settings of IGALO algorithm and the parameter settings are presented in Table 2. Mutation rate 30%…”

Section: Methodsmentioning

confidence: 99%

“…al., proposes local search mutation strategy on the basis of proved local monotonicity of function Q and completes the task of community detection in complex networks in combination with the locus-based adjacency encoding strategy and uniform crossover strategy. MIGA algorithm [15], proposed by Shang et. al., optimizes the elite individuals through simulated annealing algorithm on the basis of genetic algorithm, so as to obtain better community structure.…”

Section: Introductionmentioning

confidence: 99%

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Community Detection in Complex Networks based on Improved Genetic Algorithm and Local Optimization

Deng¹,

Liu²

2016

IJGDC

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Community Detection in Complex Networks based on Improved Genetic Algorithm and Local Optimization

Deng¹,

Liu²

2016

IJGDC

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“…Duch and Arenas (2005) used the extremal optimization method to make an approach to the largest modularity. Shang et al (2013) presented an improved genetic algorithm MIGA to optimize the modularity of community structure in the network.…”

Section: Single-objective Community Detectionmentioning

confidence: 99%

“…Modularity (Newman and Girvan 2004) is the most known optimization objective function used for evaluating the significance of community structure in the network. In recent years, numerous optimization methods have been proposed to maximize the modularity of community structure in the network, including FN (Newman 2004b), CNM (Clauset et al 2004) extremal optimization (Duch and Arenas 2005), and genetic algorithm (Shang et al 2013). However, the researches by Fortunato and Barthélemy (2007) have shown that the modularity optimization-based methods may fail to find communities which are smaller than a certain size.…”

Section: Introductionmentioning

confidence: 99%

Overlapping community detection in complex networks using multi-objective evolutionary algorithm

Zhao

Feng

2015

Comp. Appl. Math.

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Community structure is an important topological property of complex networks, which has great significance for understanding the function and organization of networks. Generally, community detection can be formulated as a single-objective or multi-objective optimization problem. Most existing optimization-based community detection algorithms are only applicable to disjoint community structure. However, it has been shown that in most real-world networks, a node may belong to multiple communities implying overlapping community structure. In this paper, we propose a multi-objective evolutionary algorithm for identifying overlapping community structure in complex networks based on the framework of non-dominated sorting genetic algorithm. Two negatively correlated evaluation metrics of community structure, termed as negative fitness sum and unfitness sum, are adopted as the optimization objectives. In our algorithm, link-based adjacency representation of overlapping community structure and a population initialization method based on local expansion are proposed. Extensive experimental results on both synthetic and real-world networks demonstrate that the proposed algorithm is effective and promising in detecting overlapping community structure in complex networks.

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“…Many traditional community detecting methods hold that each node can only belong to one community, such as Modularity optimization [1], [2], Hierarchical clustering [3], [4], Spectral Algorithms [5], [6], label propagation algorithm [7], [8], Methods based on statistical inference [9]. However in some real networks, communities are not independent , nodes can belong to more than one community ,which will lead to overlapping communities .…”

Section: Relate Workmentioning

confidence: 99%

A Fast Method of Detecting Overlapping Community in Network Based on LFM

Li¹,

Zhu²

2015

JSW

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Detect overlapping communities efficiently and effectively in various social networks has been more and more important. Aiming at the high complexity of expanding strategy and the defect of generating many homeless nodes in LFM, we propose a quick algorithm based on local optimization of a fitness function(QLFM). The proposed algorithm firstly select a node as seed randomly .With a local fitness function ,the algorithm then will expand from inside to outside of the seed according to the Breadth-First-Search in graph. As different seeds will expand to different communities independently ,and these communities have same nodes ,thus our method can detect overlapping nodes quickly and efficiently. An empirical evaluation of the method using real and synthetic datasets shows that the method give better result not only in time efficiency, but also in quality aspect than other methods at the overlapping community detection.

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Community detection based on modularity and an improved genetic algorithm

Cited by 179 publications

References 31 publications

Community Detection in Complex Networks based on Improved Genetic Algorithm and Local Optimization

Community Detection in Complex Networks based on Improved Genetic Algorithm and Local Optimization

Overlapping community detection in complex networks using multi-objective evolutionary algorithm

A Fast Method of Detecting Overlapping Community in Network Based on LFM

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