mayabechlerspeicher/Graph-Neural-Additive-Networks---GNAN
By leveraging Generalized Additive Models and adapting them to graph data, GNAN allows you to visualize exactly what your model learns—offering transparent explanations for the model and …
GNaN: A natural neighbor search algorithm based on universal ...
2024年2月1日 · Inspired by Newton’s law of universal gravitation, we propose a NaN search algorithm based on universal gravitation (GNaN-Searching). Our algorithm calculates …
The Intelligible and Effective Graph Neural Additive Network
We demonstrate the intelligibility of GNANs in a series of examples on different tasks and datasets. In addition, we show that the accuracy of GNAN is on par with black-box GNNs, …
GNaN-searching: 受牛顿万有引力定律的启发，提出 ... - Gitee
Inspired by Newton’s law of universal gravitation, we propose a NaN search algorithm based on universal gravitation (GNaN-Searching). Our algorithm calculates gravitation using the …
GNaN:一种基于万有引力的自然邻居搜索算法-论论
2023年11月3日 · 本研究提出了一种基于万有引力的自然邻居搜索算法（GNaN-Searching），该算法使用数据点的结构特征计算引力，并将引力作为邻居判断标准 智能科学信息平台
Graph-Neural-Additive-Networks---GNAN/GNAN.py at main
class GNAN(nn.Module): def __init__(self, in_channels, out_channels, n_layers, hidden_channels=None, bias=True, dropout=0.0, device='cpu', normalize_rho=True, …
The Intelligible and Effective Graph Neural Additive Networks
Our model, Graph Neural Additive Network (GNAN), is a novel extension of the interpretable class of Generalized Additive Models, and can be visualized and fully understood by humans. …
数据分析团队: 数据挖掘、机器学习和数据分析团队，相关论文代 …
GNaN-searching 受牛顿万有引力定律的启发，提出了一种基于万有引力的NaN搜索算法。 该算法利用数据集中数据点的结构特征来计算引力，利用数据间的引力作为邻居判断准则。
The Intelligible and Effective Graph Neural Additive Networks
Our model, Graph Neural Additive Network (GNAN), is a novel extension of the interpretable class of Generalized Additive Models, and can be visualized and fully understood by humans. …
GNaN: A natural neighbor search algorithm based on
Download Citation | On Oct 1, 2023, Juntao Yang and others published GNaN: A natural neighbor search algorithm based on universal gravitation | Find, read and cite all the research you need …