Attention aware edge-node exchange graph neural network

Ruiqin WANG; Yimin HUANG; Qishun JI; Chaoyi WAN; Zhifeng ZHOU

doi:10.11959/j.issn.1000-0801.2024017

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Attention aware edge-node exchange graph neural network

Research and Development | 更新时间：2024-06-05

- Attention aware edge-node exchange graph neural network
- Telecommunications Science Vol. 40, Issue 1, Pages: 106-114(2024)
- 作者机构：
  
  1. 湖州师范学院信息工程学院，浙江湖州 313000
  2. 温州大学图书馆，浙江温州 325035
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2024017
  CLC： TP393
- Published Online：2024-01，
  
  Published：20 January 2024
- 稿件说明：
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Ruiqin WANG, Yimin HUANG, Qishun JI, et al. Attention aware edge-node exchange graph neural network[J]. Telecommunications science, 2024, 40(1): 106-114.
DOI：

Ruiqin WANG, Yimin HUANG, Qishun JI, et al. Attention aware edge-node exchange graph neural network[J]. Telecommunications science, 2024, 40(1): 106-114. DOI： 10.11959/j.issn.1000-0801.2024017.

摘要

提出了一种注意力感知的边-节点交换图神经网络（attention aware edge-node exchange graph neural network，AENN）模型，在图结构化数据表示框架下，使用边-节点切换卷积的图神经网络算法进行图编码，用于半监督分类和回归分析。AENN 是一种通用的图编码框架，用于将图节点和边嵌入一个统一的潜在特征空间。具体地，基于原始无向图，不断切换边与节点的卷积，并在卷积过程中通过注意力机制分配不同邻居的权重，从而实现特征传播。在 3 个数据集上的实验研究表明，所提方法较已有方法在半监督分类和回归分析中具有明显的性能提升。

Abstract

An attention aware edge-node exchange graph neural network (AENN) model was proposed

which used edge-node switched convolutional graph neural network method for graph encoding in a graph structured data representation framework for semi supervised classification and regression analysis.AENN is an universal graph encoding framework for embedding graph nodes and edges into a unified latent feature space.Specifically

based on the original undirected graph

the convolution between edges and nodes was continuously switched

and during the convolution process

attention mechanisms were used to assign weights to different neighbors

thereby achieving feature propagation.Experimental studies on three datasets show that the proposed method has significant performance improvements in semi-supervised classification and regression analysis compared to existing methods.

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references

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