Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based o...Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based on graph convolutional network(GCN).Methods Clauses that contain symptoms,formulas,and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs,which were used to propose a node representation learning method based on GCN−the Traditional Chinese Medicine Graph Convolution Network(TCM-GCN).The symptom-formula,symptom-herb,and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes,and thus acquiring the nodes’sum-aggregations of symptoms,formulas,and herbs to lay a foundation for the downstream tasks of the prediction models.Results Comparisons among the node representations with multi-hot encoding,non-fusion encoding,and fusion encoding showed that the Precision@10,Recall@10,and F1-score@10 of the fusion encoding were 9.77%,6.65%,and 8.30%,respectively,higher than those of the non-fusion encoding in the prediction studies of the model.Conclusion Node representations by fusion encoding achieved comparatively ideal results,indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.展开更多
Graph contrastive learning(GCL)has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner.Existing GCL methods com...Graph contrastive learning(GCL)has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner.Existing GCL methods commonly adopt predefined graph augmentations to generate two contrastive views.Subsequently,they design a contrastive pretext task between these views with the goal of maximizing their agreement.These methods as-sume the augmented graph can fully preserve the semantics of the original.However,typical data augmentation strategies in GCL,such as random edge dropping,may alter the properties of the original graph.As a result,previous GCL methods overlooked graph differences,potentially leading to difficulty distinguishing between graphs that are structurally similar but semantically different.Therefore,we argue that it is necessary to design a method that can quantify the dissimilarity between the original and augmented graphs to more accurately capture the relationships between samples.In this work,we propose a novel graph contrastive learning framework,named Accurate Difference-based Node-Level Graph Contrastive Learning(DNGCL),which helps the model distinguish similar graphs with slight differences by learning node-level differences between graphs.Specifically,we train the model to distinguish between original and augmented nodes via a node discriminator and employ cosine dissimilarity to accurately measure the difference between each node.Furthermore,we employ multiple types of data augmentation commonly used in current GCL methods on the original graph,aiming to learn the differences between nodes under different augmentation strategies and help the model learn richer local information.We conduct extensive experiments on six benchmark datasets and the results show that our DNGCL outperforms most state-of-the-art baselines,which strongly validates the effectiveness of our model.展开更多
基金New-Generation Artificial Intelligence-Major Program in the Sci-Tech Innovation 2030 Agenda from the Ministry of Science and Technology of China(2018AAA0102100)Hunan Provincial Department of Education key project(21A0250)The First Class Discipline Open Fund of Hunan University of Traditional Chinese Medicine(2022ZYX08)。
文摘Objective To construct symptom-formula-herb heterogeneous graphs structured Treatise on Febrile Diseases(Shang Han Lun,《伤寒论》)dataset and explore an optimal learning method represented with node attributes based on graph convolutional network(GCN).Methods Clauses that contain symptoms,formulas,and herbs were abstracted from Treatise on Febrile Diseases to construct symptom-formula-herb heterogeneous graphs,which were used to propose a node representation learning method based on GCN−the Traditional Chinese Medicine Graph Convolution Network(TCM-GCN).The symptom-formula,symptom-herb,and formula-herb heterogeneous graphs were processed with the TCM-GCN to realize high-order propagating message passing and neighbor aggregation to obtain new node representation attributes,and thus acquiring the nodes’sum-aggregations of symptoms,formulas,and herbs to lay a foundation for the downstream tasks of the prediction models.Results Comparisons among the node representations with multi-hot encoding,non-fusion encoding,and fusion encoding showed that the Precision@10,Recall@10,and F1-score@10 of the fusion encoding were 9.77%,6.65%,and 8.30%,respectively,higher than those of the non-fusion encoding in the prediction studies of the model.Conclusion Node representations by fusion encoding achieved comparatively ideal results,indicating the TCM-GCN is effective in realizing node-level representations of heterogeneous graph structured Treatise on Febrile Diseases dataset and is able to elevate the performance of the downstream tasks of the diagnosis model.
基金supported in part by the Zhejiang Provincial Natural Science Foundation of China(LDT23F01012F01 and LDT23F01015F01)in part by the Fundamental Research Funds for the Provincial Universities of Zhejiang Grant GK229909299001-008the National Natural Science Foundation of China(62372146 and 61806061).
文摘Graph contrastive learning(GCL)has attracted extensive research interest due to its powerful ability to capture latent structural and semantic information of graphs in a self-supervised manner.Existing GCL methods commonly adopt predefined graph augmentations to generate two contrastive views.Subsequently,they design a contrastive pretext task between these views with the goal of maximizing their agreement.These methods as-sume the augmented graph can fully preserve the semantics of the original.However,typical data augmentation strategies in GCL,such as random edge dropping,may alter the properties of the original graph.As a result,previous GCL methods overlooked graph differences,potentially leading to difficulty distinguishing between graphs that are structurally similar but semantically different.Therefore,we argue that it is necessary to design a method that can quantify the dissimilarity between the original and augmented graphs to more accurately capture the relationships between samples.In this work,we propose a novel graph contrastive learning framework,named Accurate Difference-based Node-Level Graph Contrastive Learning(DNGCL),which helps the model distinguish similar graphs with slight differences by learning node-level differences between graphs.Specifically,we train the model to distinguish between original and augmented nodes via a node discriminator and employ cosine dissimilarity to accurately measure the difference between each node.Furthermore,we employ multiple types of data augmentation commonly used in current GCL methods on the original graph,aiming to learn the differences between nodes under different augmentation strategies and help the model learn richer local information.We conduct extensive experiments on six benchmark datasets and the results show that our DNGCL outperforms most state-of-the-art baselines,which strongly validates the effectiveness of our model.
