stemgnn原理

stemgnn原理
## StemGNN: A Stemmed Graph Neural Network for Semi-Supervised Learning ##。

### Abstract:
StemGNN is a novel graph neural network architecture that addresses the challenges of semi-supervised learning on graphs. It combines the strengths of stem graphs and graph neural networks to exploit both the structural and label information in a unified framework. StemGNN leverages the concept of stems, which are subgraphs induced by each data instance, to capture the local neighborhood structure. It then employs a graph neural network to propagate information across the stemmed graphs, incorporating both labeled and unlabeled instances. Extensive experiments demonstrate StemGNN's superior performance in semi-supervised node classification tasks on various real-world datasets.
StemGNN is a graph neural network architecture that combines the strengths of stem graphs and graph neural networks. It is designed for semi-supervised learning on graphs, where only a small portion of the data is labeled. StemGNN leverages the concept of stems, which are subgraphs induced by each data instance, to capture the local neighborhood structure. It then employs a graph neural network to propagate information across the stemmed graphs, incorporating both labeled and unlabeled instances.
StemGNN has several advantages over existing graph neural network architectures for semi-supervised learning. First, it explicitly models the local neighborhood
structure of each data instance using stems. This allows StemGNN to capture important structural information that is often overlooked by other graph neural network architectures. Second, StemGNN incorporates both labeled and unlabeled instances into the learning process. This allows StemGNN to exploit the unlabeled data to improve the generalization performance of the model. Third, StemGNN is scalable to large graphs. The use of stems allows StemGNN to focus on the local neighborhood structure of each data
instance, which reduces the computational cost of training the model.
StemGNN has been evaluated on a variety of real-world datasets for semi-supervised node classification. The results show that StemGNN outperforms existing graph neural network architectures in terms of accuracy and robustness.
### StemGNN: 一种用于半监督学习的茎图神经网络 ##。

中文回答：
StemGNN 是一种图神经网络架构，它结合了茎图和图神经网络的优点。

它旨在用于图上的半监督学习，其中只有少部分数据被标记。

StemGNN 利用茎的概念，茎是每个数据实例诱发的子图，以捕获局部邻域结构。

然后，它采用图神经网络来跨茎图传播信息，并结合标记和未标记的实例。

StemGNN 相对于现有的用于半监督学习的图神经网络架构具有以下几个优点。

首先，它使用茎明确地对每个数据实例的局部邻域结构进行建模。

这使得 StemGNN 能够捕获重要的结构信息，而这些信息通常被其他图神经网络架构忽略。

其次，StemGNN 在学习过程
中同时结合了标记和未标记的实例。

这使得 StemGNN 能够利用未标
记的数据来提高模型的泛化性能。

第三，StemGNN 可扩展到大型图。

使用茎使得 StemGNN 能够专注于每个数据实例的局部邻域结构，从
而降低了训练模型的计算成本。

StemGNN 已在各种用于半监督节点分类的真实世界数据集上得
到评估。

结果表明，StemGNN 在准确性和鲁棒性方面优于现有的图
神经网络架构。