Currently, graph convolutional network (GCN) is widely used in relation extraction (RE) tasks. Within RE tasks in the form of directed graphs, the placement of entities in the sentence context generates a large number of remote entity nodes in the directed graph. However, the...
Graph Convolutional Network View all Topics Recommended publications Expert Systems with Applications Journal Neurocomputing Journal Discrete Applied Mathematics Journal Pattern Recognition JournalBrowse books and journals Featured Authors Friedrich, Tobias Hasso-Plattner-Institut für Softwaresystemtechnik GmbH, ...
For performance analysis and comparison, we use two GNNs variants: Graph Convolutional Network (GCN) and Graph Attention Network (GAT). GCN is a multilayer connected neural network architecture used to learn low-dimensional node representa- tions from graph-structured data39. Through direct ...
在每一次图卷积操作之后,通过融合邻域节点的特征来更新每个节点的信息。 (下面介绍加权图卷积网络WGCN,Weighted graph convolutional network.) WGCN与vanilla GCN的对比 图3,WGCN与vanilla GCN的对比。以上左图中的节点a为例。一开始,节点a仅包含其本身的特征。在1层GCN之后,如上右图所示,节点a获得邻接节点c,h和...
为了解决这些问题,我们提出了一种新的加权图卷积网络模型weighted graph convolutional network model(WGCN)。在该模型中,我们在依赖树中加入虚边virtual edges 来构造一个逻辑邻接矩阵 logical adjacency matrix(LAM),它只需要一层WGCN就可以直接求出k阶邻域依赖k-order neighborhood dependence。我们利用WGCN层间的残差...
(2024) proposed a socially augmented heterogeneous graph convolutional network (SHGCN) to enhance multi-behavior prediction by incorporating social information. Xie et al. (2024) proposed a multi-behavior recommendation model based on heterogeneous graphs and an attention network, which exploits the ...
For performance analysis and comparison, we use two GNNs variants: Graph Convolutional Network (GCN) and Graph Attention Network (GAT). GCN is a multilayer connected neural network architecture used to learn low-dimensional node representa- tions from graph-structured data39. Through direct ...
Graph convolutional networks for computational drug development and discovery. Brief Bioinform. 2020;21(3):919–35. Article PubMed Google Scholar Yu L, Cheng M, Qiu W, Xiao X, Lin W. idse-HE: Hybrid embedding graph neural network for drug side effects prediction. J Biomed Inform. 2022;...
An attentive joint model with transformer-based weighted graph convolutional network for extracting adverse drug event relation 2022, Journal of Biomedical Informatics Show abstract A contextual multi-task neural approach to medication and adverse events identification from clinical text 2022, Journal of Bio...
Graph convolutional network (GCN), with its capability to update the current node features according to the features of its first-order adjacent nodes and edges, has achieved impressive performance in dependency capturing. But some important nodes from which we should figure out the dependencies are...