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Link prediction method of graph attention network based on node similarity

A link prediction and attention technology, applied in the field of graph neural network, can solve the problems of affecting the results, embedding representation cannot completely contain structural information, etc., and achieve the effect of strong expressive ability

Pending Publication Date: 2022-06-28
YANGZHOU UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This makes the embedded representation of the nodes learned by the graph attention network in the low-dimensional space unable to completely contain all the structural information, which in turn affects the results of the network's embedded representation in the link prediction task.

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  • Link prediction method of graph attention network based on node similarity
  • Link prediction method of graph attention network based on node similarity
  • Link prediction method of graph attention network based on node similarity

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Embodiment Construction

[0028] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0029] The present invention proposes a link prediction method of graph attention network based on node similarity, such as figure 1 shown, including the following steps:

[0030] Step 1: Build an undirected heterogeneous network model, and preprocess the node data in the network to obtain the adjacency matrix of the network, and then use the node similarity index to calculate the similarity matrix of the network.

[0031] Prepare an undirected network data set: including files describing the link relationship between nodes, used to form the adjacency matrix of the network, and restore the structural characteristics of the complex network. In this embodiment, a common Cora data set is selected. This data set includes 2708 nodes and 5429 edges.

[0032] Data processing: Preprocess the data of the nodes representing the link relationship between the nodes in ...

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Abstract

The invention discloses a link prediction method of a graph attention network based on node similarity, which comprises the following steps: firstly, constructing an undirected heterogeneous network model, preprocessing node data, and calculating a similarity matrix of the network; secondly, constructing a graph attention network model based on a node similarity index, and solving embedded representations of all nodes in the network; thirdly, carrying out negative sampling on information of existing edges, dividing all true edges and false edges into a training set and a test set, and using the training set and the test set in model training; and finally, inputting the embedded representations of the nodes learned through a graph attention method based on node similarity into a machine learning classifier in a series connection manner, and performing link prediction. According to the method, more related nodes are linked together through the node similarity index, so that the embedded representation learned by each node more fully represents the structural information of the network, and the embedded representation of the node obtains a better result in a downstream link prediction task.

Description

technical field [0001] The invention belongs to the field of graph neural networks, and in particular relates to a link prediction method of a graph attention network based on node similarity. Background technique [0002] In a complex network, the nodes in the network exist in a non-Euclidean space. Therefore, when representing the information of the nodes, in addition to considering the characteristic information of each node itself like the data in the Euclidean space, it is also necessary to consider the structural information between the nodes. . Excellent results can be achieved in downstream tasks such as node classification and link prediction only when the learned node embedding representation fully reflects all the feature information of nodes. Therefore, when learning the embedded representation of nodes in the network in the low-dimensional space, it is necessary to include all the information of each node as much as possible, including the node's own informatio...

Claims

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Application Information

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IPC IPC(8): H04L41/04H04L41/12H04L41/14H04L41/147G06K9/62G06N3/04G06N3/08G06N5/00G06N20/20
CPCH04L41/04H04L41/145H04L41/147H04L41/12G06N3/084G06N20/20G06N5/01G06N3/047G06N3/048G06N3/045G06F18/22G06F18/2415G06F18/24323G06F18/214
Inventor 刘渊赵紫娟杨凯
Owner YANGZHOU UNIV
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