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Social relation data generation method of generative adversarial network

A technology of relational data and social relations, which is applied in the field of social relational data generation of generative confrontation network, can solve problems such as lack of learning ability and inability to simulate the characteristics of social network data sets, and achieve rich samples, convenient simulation, and easy research Effect

Pending Publication Date: 2020-07-14
SICHUAN XW BANK CO LTD
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Problems solved by technology

[0008] However, the Barabasi-Albert data model does not have the ability to learn and cannot simulate the characteristics of a specific social network dataset, so it has limitations in practical applications

Method used

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  • Social relation data generation method of generative adversarial network
  • Social relation data generation method of generative adversarial network
  • Social relation data generation method of generative adversarial network

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Embodiment Construction

[0031] Such as Figure 1 to Figure 5 The social relationship data generation method of the generative confrontation network shown in the present invention includes:

[0032] A. Save the relationship data of a social group on the storage device, such as the social group relationship data disclosed by FaceBook: https: / / snap.stanford.edu / data / egonets-Facebook.html. Such as figure 2 As shown, the relationship data includes user node information representing nodes and user relationship information representing edges. The user node information includes the user node information matrix V={v 1 ,v 2 ,...,v n}, the user relationship information includes user relationship information adjacency matrix where v i Indicates the i-th user node, i∈[1,n], e ij Indicates whether the i-th user node is directly connected to the j-th user node.

[0033] B. Build a generator that includes a first graph neural network that receives random noise input, the initial parameters of the random no...

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Abstract

The invention relates to a social relation data generation method of a generative adversarial network. The method comprise: A, storing relation data of a social group, including user node informationand user relation information; B, constructing a generator which comprises a first graph neural network for receiving random noise and is used for generating pseudo-relation data; C, constructing a discriminator which comprises a second graph neural network for receiving the relational data and the pseudo-relational data and is used for judging the difference degree of feature structures between the relational data and the pseudo-relational data; and D, alternately training the generator and the discriminator until the discriminator judges that the feature structures of the pseudo relationshipdata and the relationship data are the same, and using the current generator to generate a social relationship data sample. According to the method, the social relation data with the same characteristics as the real relation data can be generated, the social relation data can be simulated more conveniently, the characteristics of the social relation data can be researched more conveniently, and richer samples are provided for related machine learning models of the social network relation data.

Description

technical field [0001] The invention relates to a method for generating social relationship data, in particular to a method for generating social relationship data of a generative confrontation network. Background technique [0002] Currently, there are many data models and methods for analyzing and generating social relational network data, and different data models and methods have their own emphases and deficiencies. For example, the commonly used Barabasi-Albert data model, when generating social network data, creates a random graph with n nodes to simulate the preference contact existing in social network data. [0003] The generation steps of the random graph include: [0004] Step 1: execute step 2 with probability p, otherwise execute step 3; [0005] Step 2: a new node is connected to an old node selected evenly at random; [0006] Step 3: Connect the new node to the n old nodes with a probability proportional to the n old nodes, the probability of connecting to ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/00G06N3/08G06N20/00
CPCG06Q50/01G06N20/00G06N3/08
Inventor 杨晓东
Owner SICHUAN XW BANK CO LTD
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