Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations.Most existing graph embedding methods focus on the topological struct...Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations.Most existing graph embedding methods focus on the topological structure of graph data,but ignore the semantic information of graph data,which results in the unsatisfied performance in practical applications.To overcome the problem,this paper proposes a novel deep convolutional adversarial graph autoencoder(GAE)model.To embed the semantic information between nodes in the graph data,the random walk strategy is first used to construct the positive pointwise mutual information(PPMI)matrix,then,graph convolutional net-work(GCN)is employed to encode the PPMI matrix and node content into the latent representation.Finally,the learned latent representation is used to reconstruct the topological structure of the graph data by decoder.Furthermore,the deep convolutional adversarial training algorithm is introduced to make the learned latent representation conform to the prior distribution better.The state-of-the-art experimental results on the graph data validate the effectiveness of the proposed model in the link prediction,node clustering and graph visualization tasks for three standard datasets,Cora,Citeseer and Pubmed.展开更多
中药方剂(traditional Chinese medicine formula,TCMF)是中医治疗的一种主要手段。然而一首方剂往往包含多种草药,这其中只有几种草药对治疗特定的病症起重要作用。因此,找出方剂中的核心药物和其配伍规律对研究中药方剂有非常重要的...中药方剂(traditional Chinese medicine formula,TCMF)是中医治疗的一种主要手段。然而一首方剂往往包含多种草药,这其中只有几种草药对治疗特定的病症起重要作用。因此,找出方剂中的核心药物和其配伍规律对研究中药方剂有非常重要的意义。针对该问题,提出了一种基于效用度(effect degree,ED)的核心药物及配伍规律发现方法。该方法包含三个主要步骤,分别是基于药物效用度的核心药物发现算法、基于带药对效用度的点式互信息(pointwise mutual information with herb pair ED,PMIED)的药物组网算法、基于重叠社团的高效药物配伍规律发现算法。通过实验,发现了肺痿方剂的42种核心药物和30种药物配伍,经分析和中医专家确认,42种核心药物对肺痿确有良好疗效,30组药物配伍中有26组符合药物配伍关系且对肺痿有良好疗效。展开更多
基金Supported by the Strategy Priority Research Program of Chinese Academy of Sciences(No.XDC02070600).
文摘Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations.Most existing graph embedding methods focus on the topological structure of graph data,but ignore the semantic information of graph data,which results in the unsatisfied performance in practical applications.To overcome the problem,this paper proposes a novel deep convolutional adversarial graph autoencoder(GAE)model.To embed the semantic information between nodes in the graph data,the random walk strategy is first used to construct the positive pointwise mutual information(PPMI)matrix,then,graph convolutional net-work(GCN)is employed to encode the PPMI matrix and node content into the latent representation.Finally,the learned latent representation is used to reconstruct the topological structure of the graph data by decoder.Furthermore,the deep convolutional adversarial training algorithm is introduced to make the learned latent representation conform to the prior distribution better.The state-of-the-art experimental results on the graph data validate the effectiveness of the proposed model in the link prediction,node clustering and graph visualization tasks for three standard datasets,Cora,Citeseer and Pubmed.
文摘中药方剂(traditional Chinese medicine formula,TCMF)是中医治疗的一种主要手段。然而一首方剂往往包含多种草药,这其中只有几种草药对治疗特定的病症起重要作用。因此,找出方剂中的核心药物和其配伍规律对研究中药方剂有非常重要的意义。针对该问题,提出了一种基于效用度(effect degree,ED)的核心药物及配伍规律发现方法。该方法包含三个主要步骤,分别是基于药物效用度的核心药物发现算法、基于带药对效用度的点式互信息(pointwise mutual information with herb pair ED,PMIED)的药物组网算法、基于重叠社团的高效药物配伍规律发现算法。通过实验,发现了肺痿方剂的42种核心药物和30种药物配伍,经分析和中医专家确认,42种核心药物对肺痿确有良好疗效,30组药物配伍中有26组符合药物配伍关系且对肺痿有良好疗效。
