Overlapped community division method based on K-core iteration factor and community membership degree

An iterative factor and overlapping community technology, applied in the field of overlapping community division, can solve problems such as high time complexity, unstable community division results, and low accuracy rate, so as to reduce time, improve stability and accuracy, and improve quality and the effect on accuracy

Pending Publication Date: 2019-07-05
CHONGQING UNIV
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Problems solved by technology

[0003] However, the existing technology uses random selection of seed nodes, resulting in high time complexity, whi

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  • Overlapped community division method based on K-core iteration factor and community membership degree
  • Overlapped community division method based on K-core iteration factor and community membership degree
  • Overlapped community division method based on K-core iteration factor and community membership degree

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Embodiment Construction

[0053] The present invention will be further described in detail below in conjunction with examples and specific implementation methods. However, it should not be understood that the scope of the above subject matter of the present invention is limited to the following embodiments, and all technologies realized based on the content of the present invention belong to the scope of the present invention.

[0054] figure 1 According to an exemplary embodiment of the present invention, an overlapping community division method based on K-kernel iteration factor and community membership, specifically includes the following steps:

[0055] Step S1: Read the network dataset, generate a network structure diagram and obtain the adjacency information table of each node in the network.

[0056] In this embodiment, the overlapping community division data set is first read from the network, and the network structure graph G and the adjacency information table N of each node in the network s...

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Abstract

The invention discloses an overlapped community division method based on a K-core iteration factor and a community membership degree, and the method comprises the following steps of S1, reading a network data set, generating a network structure diagram G, and obtaining an adjacent information table Nij of each node of a network; s2, calculating a K-core iteration factor of each node in the networkstructure chart G; S3, calculating the node density of each node in the network structure diagram G, and carrying out normalization processing on the node density; s4, calculating the node influenceby combining the K-core iteration factor and the node density, and selecting the node with the maximum node influence as a seed node; S5, carrying out local community expansion by taking the seed nodeas a core to obtain a sub-community, thereby obtaining a first community division set; and S6, combining the sub-communities in the first community division set to obtain a second community divisionset.

Description

technical field [0001] The invention relates to the technical field of overlapping community division, in particular to an overlapping community division method based on K-kernel iteration factor and community membership. Background technique [0002] The discovery of overlapping communities mainly takes advantage of the local extension properties of the communities. In the real world, many complex interactive relationships are often abstracted into a complex network graph to represent, nodes represent individuals in the network, and edges represent the interrelationships between nodes. Among them, some nodes belong to different communities at the same time, which is the overlapping community structure, and the community structure can also be expressed as the topological relationship of the real network to a certain extent. The application of accurate division of overlapping communities helps to better understand network topology and function. It plays an important role in...

Claims

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Application Information

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IPC IPC(8): G06Q50/00G06F16/901
CPCG06Q50/01
Inventor 朱征宇杜小东赵亮赵福强赵芳舟
Owner CHONGQING UNIV
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