graph neural networkneural architecture searchautomated machine learninggeometric deep learningIn academia and industries,graph neural networks(GNNs)have emerged as a powerful approach to graph data processing ranging from node classification and link prediction tasks to graph clustering tasks.GNN models are...
Graph Neural Architecture Searchwww.ijcai.org/proceedings/2020/195 TL;DR GraphNAS将NAS运用到 GNN 上,使用的是最经典(原始)的 NAS-RL 方法,不过在搜索空间上有一定改进,将其限制在搜索每一个独立的最佳layer,最后的网络由它们顺序拼接而成。 Methods 作者介绍一个适用于GNN的搜索空间,包含了GNN领域的sota...
Graph HyperNetworks for Neural Architecture Search Chris Zhang, Mengye Ren, Raquel Urtasun ICLR 2019 4.7 强化学习 Action Schema Networks: Generalised Policies with Deep Learning Sam Toyer, Felipe Trevizan, Sylvie Thiebaux, Lexing Xie AAAI 2018 NerveNet: Learning Structured Policy with Graph Neural Net...
NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search 论文地址: https://openreview.net/pdf?id=bBff294gqLp 代码地址: https://github.com/THUMNLab/NAS-Bench-Graph 背景 神经网络架构搜索(NAS)作为自动机器学习(AutoML)的一个重要组成部分,旨在自动的搜索神经网络结构。NAS 的研究最早可以追溯到上世...
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...
考虑到现有的图数据的多样性,GNN 在不同的数据上表现各有优劣,因此我们引入 NAS(neural architecture search)来自适应的设计拓扑结构。基于特征融合框架,我们设计了新的搜索空间(search space),并基于这个搜索空间改进了搜索算法。 在本文中,我们提出了新的方案 F2GNN(Feature Fusion GNN)从特征融合的视角来设计图...
To address this problem, we propose a graph neural network-based bearing fault detection (GNNBFD) method. The method first constructs a graph using the similarity between samples; secondly the constructed graph is fed into a graph neural network (GNN) for feature mapping, and the samples output...
Graph neural network architecture search for molecular property prediction. In Proc. IEEE International Conference on Big Data 1346–1353 (IEEE, 2020). Cai, S., Li, L., Deng, J., Zhang, B., Zha, Z. J., Su, L., & Huang, Q. Rethinking Graph Neural Architecture Search from Message-...
The search space includes only fundamental functions that can handle homophilic and heterophilic graphs. The search algorithm efficiently searches for the best GNN architecture via Monte-Carlo tree search without neural models. The combination of our search space and algorithm achieves finding accurate ...
Gao, Yang, et al. "HGNAS++: Efficient Architecture Search for Heterogeneous Graph Neural Networks."IEEE Transactions on Knowledge and Data Engineering(2023). 内容 动机 现有的方法只能处理齐次图,或者使用人工设计的信息接受域来构建异构图神经网络,而不能自动选择信息接受域来自动设计异构图神经网络。