- A Dual|channel Progressive Graph Convolutional Network via ...🔍
- Dual|channel deep graph convolutional neural networks🔍
- Dual|channel Enhanced Graph Convolutional Network for Event ...🔍
- Independent Dual Graph Attention Convolutional Network for ...🔍
- [PDF] Multi|view graph convolutional networks with attention ...🔍
- A Dual|Branch Fusion of a Graph Convolutional Network and ...🔍
- Dual|channel hypergraph convolutional network for predicting herb ...🔍
- Dual Graph Convolutional Networks for Graph|Based Semi ...🔍
A Dual|channel Progressive Graph Convolutional Network via ...
A Dual-channel Progressive Graph Convolutional Network via ...
We propose a novel model called Dual-channel Progressive Graph Convolutional Network (DPGCN) via sub-graph sampling.
(PDF) A Dual-channel Progressive Graph Convolutional Network via ...
PDF | Graph Convolutional Networks (GCNs) demonstrate an excellent performance in node classification tasks by updating node representation via.
A Dual-channel Progressive Graph Convolutional Network via ...
Abstract: Graph Convolutional Networks (GCNs) demonstrate an excellent performance in node classification tasks by updating node ...
Dual-channel deep graph convolutional neural networks - Frontiers
However, current dual-channel graph convolutional neural networks are limited by the number of convolution layers, which hinders the performance improvement of ...
Dual-channel Enhanced Graph Convolutional Network for Event ...
Recent state-of-the-art (SOTA) methods have achieved improved performance in event detection by incorporating rich external information. Graph ...
Independent Dual Graph Attention Convolutional Network for ...
Through these two methods, skeleton-based action recognition can obtain and analyse temporal information and spatial information. However, joint ...
[PDF] Multi-view graph convolutional networks with attention ...
This work proposes a novel model called Dual-channel Progressive Graph Convolutional Network (DPGCN) via sub-graph sampling, which possesses superior ...
A Dual-Branch Fusion of a Graph Convolutional Network and ... - MDPI
Additionally, a spectral feature enhancement module (SFEM) enhances the transmission of crucial channel information between the two graph convolutions.
DualGCN: a dual graph convolutional network model to predict ...
However, drug resistance could not be fully discovered using these in vitro cancer cell lines. It has been revealed that tumors are highly ...
Dual-channel hypergraph convolutional network for predicting herb ...
Abstract. Herbs applicability in disease treatment has been verified through experiences over thousands of years.
Dual Graph Convolutional Networks for Graph-Based Semi ...
References. [1]. Reid Andersen, Fan Chung, and Kevin Lang. 2006. Local graph partitioning using pagerank vectors.
Rain Streak Removal via Dual Graph Convolutional Network
2019) with progressive recurrent operations. In (Wang et al. 2019), a spatial attentive network is introduced to re- move rain streaks in a local-to ...
Progressive structure enhancement graph convolutional network for ...
In contrast to traditional methods that use a fixed graph structure, our DGC module dynamically adjusts the graph structure for better data representation. By ...
Dual-Primal Graph Convolutional Networks - arXiv
[8, 20] proposed to formulate convolution-like operations in the spectral domain, defined by the eigenvectors of the graph Laplacian. A more ...
PMGCN: Progressive Multi-Graph Convolutional Network for Traffic ...
It is difficult to obtain deeper spatial information by only relying on a single adjacency matrix. In this paper, we present a progressive multi-graph ...
a dual graph convolutional network model to predict cancer drug ...
DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two ...
Progressive Feature Fusion Framework Based on Graph ...
We propose a progressive feature fusion (PFF) framework based on graph convolutional network (GCN), namely PFFGCN for RS scene classification in this article.
Dual Graph Convolutional Networks for Graph-Based Semi ...
This paper presents a simple and scalable semi-supervised learning method for graph-structured data in which only a very small portion of the training data ...
A comprehensive review of graph convolutional networks - AIMS Press
A Dual-channel Progressive Graph Convolutional Network via subgraph sampling. Electronic Research Archive, 2024, 32(7): 4398-4415. doi ...
[2202.08982] PGCN: Progressive Graph Convolutional Networks for ...
PGCN constructs a set of graphs by progressively adapting to online input data during the training and testing phases. Specifically, we ...