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Group distribution detection method based on Chebyshev filter

A Chebyshev and distribution detection technology, applied in the field of deep learning and complex network community discovery, can solve the problem of low computing efficiency, achieve the effect of improving efficiency and realizing network weight sharing

Pending Publication Date: 2019-10-08
桂林远望智能通信科技有限公司
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Problems solved by technology

[0003] Traditional complex network community discovery methods need to set more parameters, and the calculation efficiency is low

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  • Group distribution detection method based on Chebyshev filter
  • Group distribution detection method based on Chebyshev filter
  • Group distribution detection method based on Chebyshev filter

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Embodiment Construction

[0040] The group detection method based on the Chebyshev filter of the present invention will be further described below in conjunction with the accompanying drawings.

[0041] like figure 1 As shown, the present invention provides a group distribution detection method based on the Chebyshev filter.

[0042] For a complex social network formed by the college football league, the 115 college student teams participating in the competition were divided into 12 leagues, and 616 games were carried out. The specific process of group detection with the method of the present invention includes the following steps:

[0043] Step 1: Read complex network data, and abstract the complex network into a graph model, which includes nodes, edges, and adjacency weight matrix. The specific symbols are expressed as: G=(V, E, W), where V is The set of nodes represents the team, and E represents the set of nodes connecting edges, which means that two teams have played games. For each node v i ∈V,...

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Abstract

The invention provides a group distribution detection method based on a Chebyshev filter, and the method mainly comprises the following steps: reading complex network data, and abstracting a complex network into a graph model; carrying out label initialization, that is, a unique label is marked for part of nodes and used for representing a group where the nodes are located, and other nodes are notmarked; inputting the initialized nodes into a Chebyshev filter, and extracting hidden layer features of the graph model; and mapping the extracted features to a marked node space for processing, andinputting an output value into a classifier, so that the nodes with the same label are the same group. A Chebyshev filter semi-supervised learning method is used for realizing group distribution detection, fewer parameters are used for discovering the group structure of the network, and the efficiency of complex network group distribution detection is improved.

Description

technical field [0001] The invention relates to the field of deep learning and complex network community discovery, in particular to a Chebyshev filter-based community distribution detection method. Background technique [0002] In recent years, deep learning has been widely used in speech recognition, image recognition, natural language processing and other fields, but there are few applications for complex networks such as social networks, telecommunication networks, protein interaction networks, and road networks. In the past two years, graph convolutional neural network technology has made new breakthroughs. This technology can process a large amount of network data, perform convolution operations on a network, and perform coarse-graining to achieve multi-layer signal processing. [0003] Traditional community discovery methods for complex networks need to set many parameters, and the calculation efficiency is low. This method uses a graph convolutional neural network t...

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Application Information

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IPC IPC(8): G06Q50/00G06N3/04G06N3/08
CPCG06Q50/01G06N3/08G06N3/045
Inventor 蔡晓东王萌
Owner 桂林远望智能通信科技有限公司
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