Bearing fault detection by using graph autoencoder and ensemble learning Article Open access 03 March 2024 A fault diagnosis method for rolling bearing based on gram matrix and multiscale convolutional neural network Article Open access 30 December 2024 A hybrid LSTM random forest model with gr...
The guessing number of a directed graph (digraph), equivalent to the entropy of that digraph, was introduced as a direct criterion on the solvability of a network coding instance. This paper makes two contributions on the guessing number. First, we introduce an undirected graph on all possible...
Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network Lamb, "Multitask learning on graph neural networks-learning multiple graph centrality measures with a unified network," arXiv preprint arXiv:1809.07695, ... P Avelar,H Lemos,M Prates,.....
链接:https://papers.nips.cc/paper/6703-inductive-representation-learning-on-large-graphs.pdf 相关数据集: Citation Reddit PPI 是否有开源代码:有 Graph Attention Networks 作者:Petar Velickovic 发表时间:2018 发表于:ICLR 2018 标签:Inductive Graph Embedding 概述:相较于GCN/GraphSage等模型,该模型提出了图注...
Graph Neural Network Library for PyTorch. Contribute to pyg-team/pytorch_geometric development by creating an account on GitHub.
We introduced a multi-modal framework for inferring causal relations in brain networks, based on a graph neural network architecture, uniting structural and functional information observed with DTI and fMRI. First this model provides a data-driven perspective on a fundamental question in neuroscience, ...
Wang, S., et al.: Heterogeneous graph matching networks for unknown malware detection. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence, pp. 3762–3770. AAAI Press (2019) Google Scholar Xiong, C., et al.: CONAN: a practical real-time APT detection system...
Graph convolutional networks (GCNs) have recently drawn extensive attention due to their superior learning performance on graph data. Through graph convolution, topological structure and node attributes can be simultaneously aggregated in a local neighborhood. In heterogeneous information networks (HINs), th...
We propose a novel graph cross network (GXN) to achieve comprehensive feature learning from multiple scales of a graph. Based on trainable hierarchical representations of a graph, GXN enables the interchange of intermediate features across scales to promote information flow. Two key ingredients of GXN...
生物科技物理系统VIN《 Visual interaction networks: Learning a physics simulator from video》 生物科技物理系统GN《 Graph networks as learnable physics engines for inference and control》https://github.com/fxia22/gn.pytorch 生物科技分子指纹GCN《Convolutional networks on graphs for learning molecular finger...