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An Improved Density Peak Overlapping Community Discovery Method Based on Rough Set Theory

A technology of rough set theory and density peak, applied in the field of analysis and division of overlapping nodes, can solve problems such as high complexity and inability to divide overlapping nodes, and achieve the effects of reducing time overhead, ensuring accuracy, and good community structure

Active Publication Date: 2021-09-24
SOUTHWEST JIAOTONG UNIV
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Problems solved by technology

The community discovery method based on the density peak also has the problem of high complexity. Therefore, we study a data structure based on the network data set to improve the density peak algorithm and improve the efficiency of the density peak clustering algorithm for community discovery. At the same time, the overlapping More efficient identification and division of nodes is an urgent technical requirement for community discovery algorithms
However, the classic density peak clustering method cannot divide overlapping nodes

Method used

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  • An Improved Density Peak Overlapping Community Discovery Method Based on Rough Set Theory
  • An Improved Density Peak Overlapping Community Discovery Method Based on Rough Set Theory
  • An Improved Density Peak Overlapping Community Discovery Method Based on Rough Set Theory

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Embodiment Construction

[0047] The specific implementation steps are as follows:

[0048] In order to efficiently divide large-scale networks, the present invention proposes a new method for the calculation of ρ and δ and the selection of the center point in the density peak clustering algorithm, and the steps are as follows:

[0049] Step 1: Enter the network is the adjacency matrix of the network. Each node in the computing network (v i ) local density (ρ i ), it is necessary to consider v i The number of neighbors|neib(v i )|, also consider v i The connection strength SN between neighbors i , and finally ρ i The size of |neib(v i )| and SN i Joint decision, the calculation formula is as follows:

[0050]

[0051]

[0052] Among them, A xy Corresponding to the value of the x and y position in the adjacency matrix, P(neib(v i )) means neib(v i ) constitutes the number of edges when a complete graph is formed;

[0053] Step 2: Calculate each node in the network (v i ) of the min...

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Abstract

The invention discloses an improved density peak overlapping community discovery method based on rough set theory, which comprises the following steps: firstly, an improved node local density calculation method is used to calculate the local density attribute (ρ) of a node in the network; secondly, an improved high-efficiency The node minimum distance calculation strategy is used to calculate the minimum distance attribute (δ) of each node; for the calculation of the distance between nodes, a ND-subspace distance measurement method is defined and a new community center point selection method is proposed; finally, at the density peak On the clustering, the nodes in the network are divided into communities and the overlapping nodes in the network are iteratively calculated. The invention effectively solves the problem of overlapping node division, defines the ND-subspace distance measurement method for the calculation of the distance between nodes, and improves the density peak clustering method to more efficiently divide large-scale social networks, and can effectively solve large-scale Overlapping community partitioning problem for social networks.

Description

technical field [0001] The invention relates to the field of data mining, in particular to the analysis and division of overlapping nodes in large-scale social networks. Background technique [0002] With the continuous development of network technology, social networking has become an important way for people to communicate and interact. Nowadays, there are many online social platforms, such as Facebook, YouTube, Twitter and so on. These platforms will generate a large amount of social network data, which contains deeper structural information. A community is a group composed of closely connected individuals in the network, and the community is the embodiment of the local characteristics of the network. Mining the community structure in the network can help people further explore the knowledge contained in the network. In recent years, many studies have shown that there may be overlapping regions between communities, which are the key to inter-community connections in ne...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/9536G06K9/62G06Q50/00
CPCG06F16/9536G06Q50/01G06F18/2321
Inventor 陈红梅封云飞李天瑞桑彬彬王生武
Owner SOUTHWEST JIAOTONG UNIV
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