Multiobjective Genetic Method for Community Discovery in Complex Networks

Community detection is a fundamental work to analyze the structural and functional properties of complex networks. There are many algorithms proposed to find the optimal communities of network. In this paper, we focus on how vertex order influences the results of community detection. By using consensus clustering, we discover communities and get a consensus matrix under different vertex orders. Based on the consensus matrix, we study the phenomenon that some nodes are always allocated in the same community even with different vertex permutations. We call this group of nodes as constant community and propose a constant community detection algorithm (CCDA) to find constant communities in network. We also further study the internal properties of constant communities and find constant communities play a guiding role in community detection. Finally, a discussion of constant communities is given in the hope of being useful to others working in this field.

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Overlapping Community Discovery Based on the Combination of Node Influence and β-Connected Neighbors

International Journal of Cooperative Information Systems ◽

10.1142/s0218843019500114 ◽

2019 ◽

Vol 28 (04) ◽

pp. 1950011

Author(s):

Rongwang Chen ◽

Qingshou Wu ◽

Wenzhong Guo ◽

Kun Guo ◽

Qinze Wang

Keyword(s):

Community Structure ◽

Complex Networks ◽

Real World ◽

Community Discovery ◽

Overlapping Communities ◽

The Core ◽

Core Node ◽

Comparison Methods ◽

Overlapping Community

We propose an overlapping community discovery algorithm that combines node influence and [Formula: see text]-connected neighbors for effectively detecting the overlapping community structure of complex networks. On the basis of the node influence and [Formula: see text]-connected neighbors, our method accurately detects the core node community and uses the improved similarity between the node and community to expand the core node community. Accordingly, the discovery and optimization of network overlapping communities are realized. Experiments on artificial and real-world networks demonstrate that our method significantly and consistently outperforms other comparison methods.

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Comparison of Community Structure Partition Optimization of Complex Networks by Different Community Discovery Algorithms

Informatica ◽

10.31449/inf.v44i1.3029 ◽

2020 ◽

Vol 44 (1) ◽

Author(s):

Renjie Peng ◽

Yunxia Yao

Keyword(s):

Community Structure ◽

Complex Networks ◽

Community Discovery ◽

Discovery Algorithms

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A Community Discovery Algorithm for Complex Networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/1533/3/032076 ◽

2020 ◽

Vol 1533 ◽

pp. 032076

Author(s):

Lintao Lv ◽

Jialin Wu ◽

Hui Lv

Keyword(s):

Complex Networks ◽

Community Discovery

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Algorithm Based on the Vertex Similarity of the Network for Detecting Community Structure

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.926-930.2932 ◽

2014 ◽

Vol 926-930 ◽

pp. 2932-2937

Author(s):

Chi Zhang ◽

Li Xu ◽

Chang Liu ◽

Chun Long Fan

Keyword(s):

Community Structure ◽

Complex Networks ◽

Algorithm Design ◽

Auxiliary Information ◽

Q Value ◽

Community Discovery ◽

Network Experiment ◽

Vertex Similarity ◽

New Community

In order to quickly and accurately find the community structure of complex networks ,This article start from the similarity of the node ,Proposed a new community discovery algorithm. Introduced similar values and custom node value Q during the process of algorithm design ,Firstly , To Select the nodes with the largest similarity value by calculating the similarity between nodes ,Then to decide to join and expand the nodes by calculating the Q value is greater than 0 or not. Repeat the above process, you can get the whole network of community structure, The process does not require any auxiliary information or other seed nodes. Applied to the actual network experiment results verify the feasibility of the algorithm.

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