Graph convolutional networks: a comprehensive review
2019年11月10日 · In this survey, despite numerous types of graph neural networks, we conduct a comprehensive review specifically on the emerging field of graph convolutional networks, which is one of the most prominent graph deep learning models.
图神经网络GNN综述：《Graph Neural Networks: A Review of …
2023年3月30日 · 本文翻译自图神经网络综述：《Graph Neural Networks: A Review of Methods and Applications》全文共3.5万字，该论文系统地回顾了图神经网络（GNNs）的方法和应用，包括 图卷积网络 （GCN）、GraphSAGE、 图注意力网络 （GAT）等，为图神经网络领域的研究者和实践者提供了一个 ...
Graph neural networks: A review of methods and applications
2020年1月1日 · Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks.
A review of graph neural networks: concepts, architectures, …
2024年1月16日 · Graph neural networks (GNNs) are a type of deep learning model that can be used to learn from graph data. GNNs use a message-passing mechanism to aggregate information from neighboring nodes, allowing them to capture the complex relationships in graphs.
A comprehensive review of graph convolutional networks: …
2023年5月31日 · In this paper, we mainly survey the progress of GCNs and introduce in detail several basic models based on GCNs. First, we review the challenges in building GCNs, including large-scale graph data, directed graphs and multi-scale graph tasks.
A comparative review of graph convolutional networks for
2021年11月27日 · In this review, we not only give a detailed introduction to the structure of graph convolutional networks and data modalities used for human action recognition, but also focus on the application of GCNs in the field of human action recognition.
GNN综述：Review of Methods and Applications - 知乎 - 知乎专栏
数据增强(Data Augmentation)：论文考虑到GCN需要许多额外的标签数据集用于验证，以及卷积核局部化问题，为了解决这些问题，这篇论文提出Co-Training GCN和Self-Training GCN来扩充训练数据集。
(PDF) A review of graph neural networks: concepts, architectures ...
2024年1月16日 · GNNs facilitate the exchange of information between nodes in a graph, enabling them to understand dependencies within the nodes and edges. The paper delves into specific GNN models like...
图卷积网络(Graph Convolutional Networks, GCN)详细介绍
2021年2月17日 · 【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。
(PDF) A comprehensive review of graph convolutional networks ...
2023年5月1日 · In this paper, we mainly survey the progress of GCNs and introduce in detail several basic models based on GCNs. First, we review the challenges in building GCNs, including large-scale graph...