Fuzzy analysis of community detection in complex networks

Based on optimization modularity, many algorithms were proposed to detect community structure in complex networks. As a optimization measure, modularity has resolution limits problems. A new measure named by modularity density was introduced, which can overcome the resolution limits drawbacks of modularity function. In this paper, we propose a immune clone selection algorithm for detecting community structure in complex networks based on optimization modularity density. Immune clone selection algorithm is a stochastic searching method. It can carry out global searching effectively. Using the global searching method of immune clone selection algorithm, experiments on synthetic and real life networks proved the proposed algorithm to be an effective community detection algorithm.

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“…Compared with the real network, our result is more reasonable. It is noted that our partition is consistent with SNOWBALL algorithm [17].…”

Section: Dolphin Social Networksupporting

confidence: 79%

“…Now we do the test on real networks. The famous bottlenose dolphin network introduced by [9] is widely used as a test example for methods of identifying communities in networks [8,9,17].…”

Section: Dolphin Social Networkmentioning

confidence: 99%

Community Detection in Complex Networks Using Immune Clone Selection Algorithm

Chen

Wang

Yang

2011

JDCTA

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“…In this subsection, four classical complex networks with known community structures are selected to test the introduced algorithm. The description of these four networks can be found everywhere [1,11,16,23]. Taking Zachary Karate Club network with two communities, for example, we first choose randomly one node in each community and label it.…”

Section: Experiments On Four Real Networkmentioning

confidence: 99%

Semisupervised Community Detection by Voltage Drops

Zhang

Xie

et al. 2016

Mathematical Problems in Engineering

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Many applications show that semisupervised community detection is one of the important topics and has attracted considerable attention in the study of complex network. In this paper, based on notion of voltage drops and discrete potential theory, a simple and fast semisupervised community detection algorithm is proposed. The label propagation through discrete potential transmission is accomplished by using voltage drops. The complexity of the proposal is (| | + | |) for the sparse network with | | vertices and | | edges. The obtained voltage value of a vertex can be reflected clearly in the relationship between the vertex and community. The experimental results on four real networks and three benchmarks indicate that the proposed algorithm is effective and flexible. Furthermore, this algorithm is easily applied to graph-based machine learning methods.

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“…Researchers have proposed the community discovery method based on local information network [7,18,19,20]. In literature [7], the method of finding community structure by using the core node instead of using the initial node to calculate the local module is proposed.…”

Section: Introductionmentioning

confidence: 99%