摘要
为了解决当前图卷积网络需要依赖大型数据集,从而导致时间和空间复杂度上升问题,提出了基于自我监督学习策略的层智能图卷积网络(RRLFS-L-GCN)。首先,通过在层智能图卷积网络(layer-wise graph convolutional network,L-GCN)中添加多任务机制以提高算法的泛化能力;然后,设计一种随机删除固定步长边(aandomly remove links with a fixed step,RRLFS)的自我监督学习策略,从而提出基于自我监督学习策略的层智能图卷积网络算法;最后,通过边预测验证RRLFS-L-GCN的性能。实验结果表明,该算法的识别率最高可达97.13%。对于Cora测试集,该算法所得识别准确率比未改进的层智能图卷积网络算法提高了6.73%。对于PubMed测试集,该算法所得识别准确率比未改进的层智能图卷积网络算法提高了8.13%。与图卷积网络相比,在Citeseer数据集上,识别准确率提高了18.43%。
To solve the problem that the current graph convolutional network needs to rely on large datasets,which leads to increased time and space complexity,this research proposed a layer-wise graph convolutional network based on self-supervised learning strategy(RRLFS-L-GCN).Firstly,it added an multi-task mechanism into the layer-wise graph convolutional network(layer-wise graph convolutional network,L-GCN)to improve the generalization ability of the algorithm.Then,it designed a self-supervised learning strategy that randomly removed fixed-step links(randomly remove links with a fixed step,RRLFS).Therefore,it proposed a layer-wise graph convolutional network algorithm based on a self-supervised learning strategy.Finally,it used link prediction which was to verify the performance of RRLFS-L-GCN.Experimental results show that this algorithm has the highest recognition rate,up to 97.13%.For the Cora testset,this algorithm obtains 6.73%accuracy higher than that of the unimproved layer-wise graph convolutional network algorithm.For the PubMed testset,this algorithm obtains 8.13%accuracy higher than that of the unimproved layer-wise graph convolutional network algorithm.Compared with the graph convolutional network,it improves the recognition accuracy rate on the Citeseer dataset,which is 18.43%.
作者
孙峰
杨观赐
Ajith Kumar V
张安思
Sun Feng;Yang Guanci;Ajith Kumar V;Zhang Ansi(Experimental Teaching Center for Liberal Arts,Zhejiang Normal University,Jinhua Zhejiang 321004,China;Key Laboratory of Advanced Manufacturing Technology of Ministry of Education,Guizhou University,Guiyang 550025;School of Mechanical Engineering,Guizhou University,Guiyang 550025,China;The School of AI,Bangalore 560002,India)
出处
《计算机应用研究》
CSCD
北大核心
2022年第1期128-133,共6页
Application Research of Computers
基金
国家自然科学基金资助项目(61863005,62163007)
贵州省科技计划资助项目(黔科合支撑[2019]2814,黔科合平台人才[2020]6007,[2020]4Y056,[2021]439)
贵州省高等学校集成攻关大平台建设资助项目(黔教合KY字[2020]005)
浙江师范大学实验技术开发资助项目(SJ202123)
浙江师范大学数学化改革资助项目([2021]05)。
关键词
图卷积网络
自我监督学习策略
依赖大型数据集
层智能
多任务机制
边预测
graph convolutional network(GCN)
self-supervised learning strategy
rely on large dataset
layer-wise
multi-task mechanism
link prediction
