Community discovery method based on nonlinear non-negative matrix factorization

A technology of non-negative matrix decomposition and community discovery, applied in the field of community discovery based on nonlinear non-negative matrix decomposition, it can solve problems such as lack of learning ability and inability to improve community discovery performance, achieving the effect of strong expressive ability and improved performance

Pending Publication Date: 2022-01-07
ZHONGKAI UNIV OF AGRI & ENG
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Problems solved by technology

[0004] Chinese invention patent CN111400652A discloses "A Non-Negative Matrix Community Discovery Method and Movie Community Discovery Method", the disclosure date is July 10, 2020: including the following steps S11 data collection and calculation, forming similarity matrix X and L; S12 Decompose X into a non-negative matrix, X≈UV; S13 constructs an objective function O containing L; and S14 obtai

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  • Community discovery method based on nonlinear non-negative matrix factorization
  • Community discovery method based on nonlinear non-negative matrix factorization
  • Community discovery method based on nonlinear non-negative matrix factorization

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Experimental program
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[0042] Example 1

[0043] like figure 1 and figure 2 As shown, a community discovery method based on nonlinear non-negative matrix decomposition, including the following steps:

[0044] S1: Formation of social networks, generating no map;

[0045] S2: The root is based on the non-shadd map, and the gcn and NMF model build a unified loss function;

[0046] S3: Optimize a unified loss function to get local optimal solution;

[0047] S4: The results of the community found according to the local best solution.

[0048] In the above scheme, the introduction of the map volume network GCN and the NMF model build a unified loss function, and combined training to the NMF nonlinear characteristics, thereby improving the improvement of NMF community discovery method performance and strong expression.

[0049] Preferably, in step S1, the non-paragraph G = (V, E), where V = {V 1 , ..., v n } Collection of n network user nodes, e = {e ij| v i ∈V∧V j ∈V} is represented as a collection of connect...

Example Embodiment

[0070] Example 2

[0071] figure 2 It is the overall model frame of the method comprising an input layer, a map volume layer, and an output layer, wherein the input layer employs the adjacent matrix and the unit matrix of the graph, and the map volume layer uses a 2 layer diagram convolution network, the activation function is RELU The output layer is responsible for outputting the map node that is normalized after the SOFTMAX function represents Z and is used to rebuild the adjacency matrix. The entire model framework is trained through the reconstruction of the map and the NMF combined loss function.

Example Embodiment

[0072] Example

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Abstract

The invention relates to a community discovery method based on nonlinear non-negative matrix factorization, which comprises the following steps of: S1, formalizing a social network to generate an undirected graph; s2, according to the undirected graph, introducing a graph convolutional network GCN and an NMF model to construct a unified loss function; s3, optimizing the unified loss function to obtain a local optimal solution; and S4, extracting a community discovery result according to the local optimal solution. The graph convolutional network GCN and the NMF model are introduced to construct the unified loss function, and joint training is performed to endow the NMF with a nonlinear feature learning ability, so that the performance of the NMF-based community discovery method is improved.

Description

technical field [0001] The present invention relates to the field of convolutional networks, and more specifically, to a community discovery method based on nonlinear non-negative matrix factorization. Background technique [0002] Social networks (such as WeChat, QQ, and Weibo) commonly used by Internet users have a community structure. Users in the same community are more closely connected to each other, while users in different communities are less connected to each other. Community discovery on social networks can not only understand network structure characteristics, but also mine similar user groups, which has important application value for accurate recommendation of user products and services. [0003] At present, different types of community discovery methods have been proposed, among which community discovery methods based on Nonnegative matrix factorization (NMF) are more and more widely used because of their easy-to-interpret results, strong versatility, and mult...

Claims

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06Q50/00
CPCG06N3/08G06Q50/01G06N3/047G06N3/045
Inventor 贺超波呼增付志文郑裕龙
Owner ZHONGKAI UNIV OF AGRI & ENG
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