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Social network node classification method based on dynamic graph

A social network and node classification technology, applied in the field of data mining, can solve the problem of inability to effectively mine the dependencies between nodes affecting each other at different times, and achieve the effect of improving accuracy

Active Publication Date: 2020-06-19
GUANGDONG UNIV OF TECH
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  • Application Information

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Problems solved by technology

[0004] In order to solve the problem that in the existing social network node classification method, for the dynamically changing time series data in the social network, the mutual influence between nodes and the before and after dependencies at different times cannot be effectively mined, the present invention provides a social network based on a dynamic graph. Network Node Classification Method

Method used

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  • Social network node classification method based on dynamic graph
  • Social network node classification method based on dynamic graph
  • Social network node classification method based on dynamic graph

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Experimental program
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Effect test

Embodiment 1

[0048] Social network node classification method based on dynamic graph, such as figure 1 As shown, including the following steps:

[0049] S1. Obtain a tagged social network data set;

[0050] Use social network platforms such as Weibo and Alibaba Cloud Data Platform to collect user's original data, and remove the redundant information, and retain and extract useful information such as user characteristics, behaviors, attributes, tags, etc., to obtain social networks data set:

[0051] X=[x 1 ,x 2 ,x 3 ,...,x m ]

[0052] Where m is the number of sample data contained in the social network data set; let the sample data at time t x t ∈R n , Which contains n variables, and each sample data x t Corresponds to a label variable y t , Where y t ∈L, L represents a collection of label categories.

[0053] S2. Divide the social network data set into T time steps according to time, and construct the adjacency matrix and feature matrix of each time step according to the node attributes and node...

Embodiment 2

[0080] In order to verify the performance of the social network node classification method based on dynamic graphs proposed in Embodiment 1, the following experiments were carried out in this embodiment:

[0081] Use Weibo e-commerce data and Alibaba Cloud platform data as the test data set, where Weibo e-commerce data is homogeneous graph data, and Alibaba Cloud is heterogeneous graph data. In this embodiment, the prediction results of each social network node classification method are compared with the true labels of the test data set, and the classification accuracy rate is calculated. The value range is [0,1]. The higher the value, the better the classification effect. it is good.

[0082] 1. Comparing the social network node classification method of embodiment 1 with the static graph model NRI, GCN, GAT respectively, and the experimental results are as follows: figure 2 Shown. (MyModel in the figure indicates that the method of embodiment 1 is used), it can be seen that the ...

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Abstract

The invention discloses a social network node classification method based on a dynamic graph. The relationship of different nodes before and after a time sequence is enhanced by utilizing a sparsemaxfunction; the sparse processing and the cell gating are enabled to act together by combining the long-term and short-term memory neural network, the front-back relationship and the dependency relationship of the time series data are better mined, and the change mode of the node state in the time series data is fully expressed, so that the social network node classification accuracy is improved. The invention provides a social network node classification method. For dynamically changing time series data in a social network, the problem that the mutual influence between nodes and the dependencyrelationship before and after different time cannot be effectively mined can be solved, and the method can be used for the classification problem of dynamic structure social nodes in the fields of social platforms, recommendation systems, information systems, medical health, movie and television entertainment and the like.

Description

Technical field [0001] The present invention relates to the technical field of data mining, in particular to a method for classifying social network nodes based on dynamic graphs. Background technique [0002] Nowadays, the application of time series data of social networks in social systems, information systems, medical health, financial markets and other fields has become more and more common. Therefore, the node classification task of dynamic graphs has become an important and valuable research topic, such as product recommendation and friend recommendation. Traditional classification methods based on static graphs such as graph convolutional network (GCN) and graph attention model (GAT) are only sensitive to the static state of nodes and do not consider the relationship between nodes at different times. [0003] Another popular method is to perform a series of feature transformations on the graph nodes to mine the patterns for classification, such as multi-layer perceptron (ML...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F16/9536G06F16/2458G06K9/62G06Q50/00G06N3/04
CPCG06F16/9536G06F16/2465G06Q50/01G06F2216/03G06F18/24Y02D10/00
Inventor 蔡瑞初李烁郝志峰温雯吴迪许柏炎
Owner GUANGDONG UNIV OF TECH
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