2024年12月19日 · The SI-GCN model incorporates several innovative components: a Specific-aspect GCN module that effectively captures sentiment features for individual aspects; a knowledge-enhanced heterogeneous graph designed to manage implicit sentiment expressions and multi-word aspects; and a dual affine attention mechanism that accurately models inter ...

To deal with this problem, we propose a novel structure-induced graph convolution network (Si-GCN) framework to boost the performance of the skeleton-based action recognition task. Given a video sequence of human skeletons, the Si-GCN can produce the sample-wise category in an end-to-end way.

The SI-GCN model incorporates several innovative components: a Specific-aspect GCN module that effectively captures sentiment features for individual aspects; a knowledge-enhanced heterogeneous graph designed to manage implicit sentiment expressions and multi-word aspects; and a dual affine attention mechanism that accurately models inter ...

2019年7月1日 · In order to improve the effectiveness of spatial–temporal feature extraction from skeleton sequence, a SlowFast graph convolution network (SF-GCN) is proposed by implementing the architecture of...

首先我们给出gcn的符号定义： 对于一个图 G=（V, E） ，输入 X 是一个 N\times D 的矩阵，表示每个节点的特征，同时有图的邻接矩阵 A 。 我们希望得到一个 N\times F 的特征矩阵 Z ，表示学习到的每个节点的特征表示， F 是我们希望得到的表示的维度。

2019年7月25日 · In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search ...

2019年5月20日 · In this paper, we propose Cluster-GCN, a novel GCN algorithm that is suitable for SGD-based training by exploiting the graph clustering structure. Cluster-GCN works as the following: at each step, it samples a block of nodes that associate with a dense subgraph identified by a graph clustering algorithm, and restricts the neighborhood search ...

simp-gcn的切入点是gcn中的结点相似度保留机制。 通过理论与实验分析，证明GCN在保留结点属性特征方面确实存在提升的空间，在此基础上，提出了融合拓扑结构特征与属性特征的聚合方法，并通过引入配对相似度保留的损失函数，进一步放大结点属性特征对最终 ...

【Geom-GCN】现有的MPNNs方法具有两个基本弱点：①丢失邻域节点的结构信息；②缺乏捕获非同配性图的长距离依赖的能力。 本文从经典神经 网络 和 网络 几何学的观察出发，提出了一种新的几何聚合方案，该方案利用图背后的连续空间进行聚合，以克服上述弱点。

In this paper, we propose a novel location semantic inference with graph convolutional networks (SI-GCN). We introduce node2vec and variational autoencoder to learn spatial and temporal...