下图与baselines的比较结果显示DGCRN在三个数据集的大多数任务上均取得了最好的结果。 从对比试验可以看出,DGCRN模型对动态图以及hyper-network设计最为敏感。当去掉动态图也即 w/o dg;去掉hyper-network也即w/o hypernet;不再根据语义生成动态图而是生成静态图和MTGNN一样也即 dg2sg;将公式4中的点乘换为矩阵乘法...
2 Related Work 随着面向方面的情感分析的蓬勃发展,目前的研究大致可分为三类:基于注意的神经模型(Attention-Based Neural Networks)、基于句法规则的神经网络(Syntactic-Based Recurrent Neural Networks)和基于句法的图神经网络(Syntactic-Based Graph Neural Networks)。我们的工作是基于这些最近的大量努力。 基于语法的图...
this paper proposes a dynamic graph convolutional network model for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. The multi-scale feature fusion module extracts features of different scales through dilated convolution with different ratios to avoid losing global ...
a novel GNN model was developed to incorporate dynamic graph computation and feature aggregation of 2-hop neighbor nodes into graph convolution for brain network modeling.71 This dynamic GNN significantly improved the performance in ADHD diagnosis and revealed the circuit-level association between connecto...
most imputation methods miss the hidden dynamic connection associations that exist between graph nodes over time. To address the aforementioned spatiotemporal data imputation challenge, we present an attention-based message passing and dynamic graph convolution network (ADGCN). Specifically, this paper use...
Based on this thought, many creative methods have been proposed, in which Graph Convolution Network (GCN) based methods have shown promising performance. However, these methods depend on the graph construction, which mainly uses the prior knowledge of the road network. Recently, some works realized...
Subsequently, we construct a network designed for the second stage of training, which is built on the foundation of dynamic graph convolution. Conclusions To evaluate its effectiveness, the performance of the DGCPPISP model is scrutinized using two benchmark datasets. The ensuing results demonstrate ...
Finally, a residual structure is applied to enable our graph network to go as deep as CNNs models. The graph nodes (superpixels) inherently belonging to the same class will be ideally clustered together in the learned embedding space. Experiments show that this work is a good practice for ...
论文题目:Dynamic Semantic-Based Spatial Graph Convolution Network for Skeleton-Based Human Action Recognition 作者&团队:Xie J, Meng Y, Zhao Y, et al. 1.分布式算法博士培训中心,电子电气工程与计算机科学学院,英国利物浦大学 2.眼科与视觉科学系,英国利物浦大学,英国利物浦 ...
链接:GC-LSTM: Graph Convolution Embedded LSTM for Dynamic Link Prediction 动态链接预测是复杂网络领域的研究热点,尤其是它在生物学,社会网络,经济和工业中的广泛应用。与静态链接预测相比,动态链接要困难得多,因为网络结构会随着时间而发展。当前,大多数研究集中在静态链接预测上,该静态链接预测无法在动态网络中实现...