针对忽视局部关系中的二阶标记关系问题,本文提出了一种基于全局和局部关系的类属特征多标记分类(global and lo⁃cal relationships based on multi⁃label classification algorithm with label⁃specific features,LFGML)算法。通过全...针对忽视局部关系中的二阶标记关系问题,本文提出了一种基于全局和局部关系的类属特征多标记分类(global and lo⁃cal relationships based on multi⁃label classification algorithm with label⁃specific features,LFGML)算法。通过全局关系的角度来获取类属特征,使用加权平均法计算每个实例的邻域信息,利用杰卡德相似度提取局部关系中的二阶标记关系。LFGML算法在10个多标记数据集Genbase、Medical、Arts、Health、Flags、Cal500、Yeast、Image、Education和Emotions进行了实验。结果表明,所提出的算法相对于其他对比算法在多标记分类中具有明显的的性能优势。展开更多
Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor nodes.However,this weighting mechanism is learned ba...Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor nodes.However,this weighting mechanism is learned based on static information,which means it is susceptible to noisy nodes and edges,resulting in significant limitations.In this paper,a method is proposed to obtain context dynamically based on random walk,which allows the context-based weighting mechanism to better avoid noise interference.Furthermore,the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT)network to form a model based on a mixed weighting mechanism.The model is named as the context-based and content-based graph convolutional network(CCGCN).CCGCN can better discover important neighbors,eliminate noise edges,and learn node embedding by message passing.Experiments show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.展开更多
文摘针对忽视局部关系中的二阶标记关系问题,本文提出了一种基于全局和局部关系的类属特征多标记分类(global and lo⁃cal relationships based on multi⁃label classification algorithm with label⁃specific features,LFGML)算法。通过全局关系的角度来获取类属特征,使用加权平均法计算每个实例的邻域信息,利用杰卡德相似度提取局部关系中的二阶标记关系。LFGML算法在10个多标记数据集Genbase、Medical、Arts、Health、Flags、Cal500、Yeast、Image、Education和Emotions进行了实验。结果表明,所提出的算法相对于其他对比算法在多标记分类中具有明显的的性能优势。
基金Supported by the Natural Science Foundation of Xiamen (3502Z20227067)。
文摘Graph conjoint attention(CAT)network is one of the best graph convolutional networks(GCNs)frameworks,which uses a weighting mechanism to identify important neighbor nodes.However,this weighting mechanism is learned based on static information,which means it is susceptible to noisy nodes and edges,resulting in significant limitations.In this paper,a method is proposed to obtain context dynamically based on random walk,which allows the context-based weighting mechanism to better avoid noise interference.Furthermore,the proposed context-based weighting mechanism is combined with the node content-based weighting mechanism of the graph attention(GAT)network to form a model based on a mixed weighting mechanism.The model is named as the context-based and content-based graph convolutional network(CCGCN).CCGCN can better discover important neighbors,eliminate noise edges,and learn node embedding by message passing.Experiments show that CCGCN achieves state-of-the-art performance on node classification tasks in multiple datasets.
