天津工业大学学报2024，Vol.43Issue(3)：58-65,8.DOI:10.3969/j.issn.1671-024x.2024.03.008
基于图结构增强的图神经网络方法
Graph neural network method based on graph structure enhancement
摘要
Abstract
In response to the problem of sudden performance degradation in graph convolutional networks(GCNs)facing low homogeneity graph structures,a novel graph structure enhancement method is proposed for learning im-proved graph node representations.Firstly,the node information is propagated and aggregated by messages to obtain an initial representation of the nodes.Then the similarity metric of the node representation is calculated to obtain the homogeneous structure of the graph.Finally,the original structure of the graph and the homogeneous structure are fused for node information transfer to obtain the node representation for downstream tasks.The re-sults show that the proposed algorithm outperforms the comparison algorithm in several metrics of node classifica-tion on six publicly available datasets,especially on the four datasets with low homogeneity,the ACC scores of the proposed algorithm exceed the highest benchmark by 5.53％,6.87％,3.08％and 4.00％,and the Fl values exceed the highest benchmark by 5.75％,8.06％,6.46％and 5.61％,respectively,obtaining superior perfor-mance well above the benchmark,indicating that the proposed method successfully improves the structure of graph data and verifies the effectiveness of the algorithm for graph structure optimization.关键词
图结构增强/相似性度量/图卷积网络/节点分类Key words
graph structure enhancement/similarity measure/graph convolution network/node classification分类
计算机与自动化引用本文复制引用
张芳,单万锦,王雯..基于图结构增强的图神经网络方法[J].天津工业大学学报,2024,43(3):58-65,8.基金项目
国家自然科学基金资助项目(61702296) （61702296）
