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Graph Neural Network Library for PyTorch. Contribute to pyg-team/pytorch_geometric development by creating an account on GitHub.
Label-Consistency based Graph Neural Networks for Semi-supervised Node Classification BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation Graph-Based Semi-Supervised Learning: A Comprehensive Review K-Core Based Temporal Graph Convolutional Network for Dynamic Graphs ...
Quantifying Explainers of Graph Neural Networks in Computational Pathology(CVPR 2021) 深入学习方法的可解释性对于促进数字病理学的临床应用是必要的。然而,目前流行的基于像素处理的深度学习方法和解释技术(explainers)忽视了生物实体的概念,从而使病理学家的理解复杂化。在这项工作中,我们通过采用基于生物实体的图形...
这里的编码模型指通过特定模型架构对实体和关系的交互进行编码的模型。模型包括线性/双线性(linear/bilinear)模型、分解(factorization)模型和神经网络(neural network)模型。 (1)线性模型:通过将头部实体投影到靠近尾部实体的表征空间中,将关系表述为线性或双线性映射。
Graph Neural Network(GNN)最全资源整理分享 GNN自去年起,一直是研究的热点,图神经网络相关的关键词频繁出现在今年各大AI顶会论文title中,加深对这一领域的了解是非常必要的。分享一篇,关于GNN,目前看到的整理得最细致的资源列表。 内容涉及节点表示学习、知识图谱表示学习、图神经网络介绍、图神经网络应用、图生成...
Although deep learning (DL) models for Protein–Protein Interaction (PPI) prediction have been studied extensively, it has not yet been developed for simulating the natural PPI hierarchy. Here, we suggest HIGH-PPI, a hierarchical graph neural network, for accurate and interpretable PPI prediction. ...
vious random-walk-based approaches, employing neural net- works on graphs has been studied extensively in recent years. Using an information diffusion mechanism, the graph neural network (GNN) model updates states of the nodes and prop- agate them until a stable equilibrium [Scarselli et al., ...
这里的编码模型指通过特定模型架构对实体和关系的交互进行编码的模型。模型包括线性/双线性(linear/bilinear)模型、分解(factorization)模型和神经网络(neural network)模型。 (1)线性模型:通过将头部实体投影到靠近尾部实体的表征空间中,将关系表述为线性或双线性映射。
Existing methods for fine-scale air quality assessment have significant gaps in their reliability. Purely data-driven methods lack any physically-based mechanisms to simulate the interactive process of air pollution, potentially leading to physically inc