首页|Unbalanced Graph Multi-Scale Fusion Node Classification Method

Unbalanced Graph Multi-Scale Fusion Node Classification Method

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
Graphs are used as a data structure to describe complex relationships between things.The node classification method based on graph network plays an important role in practical applications.None of the existing graph node classification methods consider the uneven distribution of node labels.In this paper,a graph convolution algorithm on a directed graph is designed for the distribution of unbalanced graph nodes to realize node classification based on multi-scale fusion graph convolution network.This method designs different propagation depths for each class according to the unbalance ratio on the data set,and different aggregation functions are designed at each layer of the graph convolutional network based on the class propagation depth and the graph adjacency matrix.The scope of information dissemination of positive samples is expanded relatively,thereby improving the accuracy of classification of unbalanced graph nodes.Finally,the effectiveness of the algorithm is verified through experiments on the public text classification datasets.

node classificationunbalanced learningtext classification

张静克、何新林、戚宗锋、马超、李建勋

展开 >

State Key Laboratory of CEMEE,Luoyang 471003,Henan,China

School of Electronic Information and Electrical Engineering,Shanghai Jiao Tong University,Shanghai 200240,China

National Natural Science Foundation of ChinaNational Key Research and Development ProgramSpecial Research Projects for Civil AircraftProject of CEMEE

616732652020YFC1512203MJ-2017-S-382019K0302A

2024

10.1007/s12204-022-2543-1

上海交通大学学报(英文版)
上海交通大学

上海交通大学学报(英文版)

影响因子：0.151
ISSN：1007-1172
年,卷(期)：2024.29(3)