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A method for community detection in complex networks based on information theory

A complex network and information theory technology, applied in the field of community structure discovery in complex networks based on information theory, can solve problems such as high algorithm complexity, limited function, and result changes, achieving low algorithm complexity, fast operation process, The effect of small amount of computation

Active Publication Date: 2016-07-06
光谷技术有限公司
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  • Application Information

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Problems solved by technology

[0004] Existing technologies have proposed detection methods for complex network community structures, such as 201010613184.0, 201010165418.X and 201210154812.2. These methods basically discuss the detection of community structures from the degree of network nodes. However, these methods have some shortcomings. The complexity of the algorithm is often too high to meet the computing needs of large-scale networks; second, there is no specific indicator to judge the optimal number of communities; third, the obtained results may be unstable, That is to say, for the original network, some small connection changes may lead to huge changes in the results
These shortcomings may limit its function in engineering applications, and new technical solutions are needed to make up for it

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  • A method for community detection in complex networks based on information theory
  • A method for community detection in complex networks based on information theory
  • A method for community detection in complex networks based on information theory

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Experimental program
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specific Embodiment approach 1

[0029] Specific implementation mode 1: This implementation mode "information theory-based network community detection method" adopts the following technical solution to realize, which is divided into six steps:

[0030] A. According to the structure and weight information of the network, number its nodes to obtain the probability matrix of the network:

[0031] The specific method of making the probability matrix is ​​as follows:

[0032] Suppose there are n nodes in a network, and number them from 1 to n, when when w ij Indicates the weight between node i and node j, when when w ij Represents the degree of node i. Note that when the network is given, this weight is determined; thus, an n×n probability matrix can be obtained, and the element p(i, j) on the i-th row and j-th column is:

[0033] ,

[0034] Obviously this matrix is ​​a symmetric matrix, with .

[0035] B. On the basis of the above processing techniques, the information loss when merging two points int...

specific Embodiment approach 2

[0048] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that according to the complex network community detection method based on information theory of the present invention, in such as figure 1 In the shown network with 6 nodes and weights, the steps of this detection method are executed one by one, and the following results are obtained:

[0049] A. According to the structure and weight information of the network, number its nodes, after performing the task of node numbering (see figure 1 ), according to step A in the content of the invention, the resulting probability matrix is ​​as follows:

[0050] .

[0051] B. According to the method of information theory, the information loss when merging two points into a community is obtained, and the information loss matrix of the merging of 6 nodes is shown in Table 1:

[0052] Table 1 Information loss matrix for merging between nodes

[0053] node pair

information loss

...

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Abstract

The invention provides a complex network community detection method based on an information theory and relates to a method for discovering a community structure in a complex network based on the information theory. The detection method comprises a step A of numbering network nodes, manufacturing a probability matrix of the network according to the structure and weight information of the network; a step B of obtaining information loss when two points are combined into a community according to a method of the information theory; a step C of finding and combining two nodes with minimum information loss, renewing the community structure of an original network; a step D of computing the modularity factor of the network after the structure is renewed; a step E of repeating the step B, the step C and the step D until the number of the community is one; and a step F of finding a corresponding community number and a corresponding community structure of a maximum modularity factor. The method has the advantages that results are objective and stable, the method can be used in the network structure with weight, a strict community number selecting standard is provided, computing time consumption is low, and the method is suitable for a large-scale network.

Description

technical field [0001] The invention belongs to the technical field of data processing and complex networks, and relates to a method for discovering community structures in complex networks based on information theory. Background technique [0002] In today's era, the rapid development of information technology represented by the Internet has brought human society into the Internet age. In the computer world, complex networks are ubiquitous, and in the real world, complex networks can also be seen everywhere, from urban road networks to aviation route networks, from VLSI to large-scale power networks. Not a concrete manifestation of complex networks. Complex networks can also be used to describe the social relationship between people, the citation relationship between papers, and so on. Complex network has become one of the most important interdisciplinary fields. Boccara gave a clear definition of complex networks: If the behavior of some network components has been unde...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F17/30
Inventor 李永立吴冲王子政郑宇宁
Owner 光谷技术有限公司
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