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Machine learning on graphs has been extensively studied in both academic and industry.
Exploring network structure, dynamics, and function using networkx
Aric A. Hagberg, Daniel A. Schult, and Pieter J. Swart · 2008
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Exploring network structure, dynamics, and function using networkx
Aric Hagberg, Pieter Swart, and Daniel S Chult · 2008
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Collective classification in network data
Prithviraj Sen et al · 2008
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Paramils: an automatic algorithm configuration framework
Frank Hutter et al · 2009
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Algorithms for hyper-parameter optimization
James Bergstra et al · 2011
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Algorithms for hyper-parameter optimization
James Bergstra et al · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P Adams · 2012
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Making a science of model search: Hyperparameter optimization in hundreds of dimensions for vision architectures
James Bergstra, Daniel Yamins, and David Cox · 2013
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Graph based anomaly detection and description: a survey
Leman Akoglu, Hanghang Tong, and Danai Koutra · 2015
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Evaluation of a tree-based pipeline optimization tool for automating data science
Randal S. Olson et al · 2016
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Representation learning on graphs: Methods and applications
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec · 2017
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Knowledge graph embedding: A survey of approaches and applications
Quan Wang et al · 2017
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Neural architecture search with reinforcement learning
Barret Zoph and Quoc V Le · 2017
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Geometric deep learning: going beyond euclidean data
Michael M Bronstein et al · 2017
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Neural message passing for quantum chemistry
Justin Gilmer et al · 2017
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Categorical reparameterization with gumbel-softmax
Eric Jang, Shixiang Gu, and Ben Poole · 2017
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The concrete distribution: A continuous relaxation of discrete random variables
Chris J Maddison, Andriy Mnih, and Yee Whye Teh · 2017
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Large-scale evolution of image classifiers
Esteban Real et al · 2017
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Networks
Mark Newman · 2018
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A survey on network embedding
Peng Cui et al · 2018
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Graph embedding techniques, applications, and performance: A survey
Palash Goyal and Emilio Ferrara · 2018
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A comprehensive survey of graph embedding: Problems, techniques, and applications
Hongyun Cai et al · 2018
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Graph neural networks: A review of methods and applications
Jie Zhou et al · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying et al · 2018
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Neural relational inference for interacting systems
T Kipf et al · 2018
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li et al · 2018
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Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting
Bing Yu, Haoteng Yin, and Zhanxing Zhu · 2018
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Taking human out of learning applications: A survey on automated machine learning
Quanming Yao et al · 2018
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Darts: Differentiable architecture search
Hanxiao Liu, Karen Simonyan, and Yiming Yang · 2018
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Efficient neural architecture search via parameters sharing
Hieu Pham et al · 2018
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Learning transferable architectures for scalable image recognition
Barret Zoph et al · 2018
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On power law growth of social networks
Chengxi Zang et al · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu et al · 2018
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Hierarchical graph representation learning with differentiable pooling
Rex Ying et al · 2018
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Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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Relational inductive biases, deep learning, and graph networks
Peter W Battaglia et al · 2018
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Gem: A python package for graph embedding methods
Palash Goyal and Emilio Ferrara · 2018
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Graph hypernetworks for neural architecture search
Chris Zhang, Mengye Ren, and Raquel Urtasun · 2018
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Adversarial attack and defense on graph data: A survey
Lichao Sun et al · 2018
Cited alongside, same era.
Proxylessnas: Direct neural architecture search on target task and hardware
Han Cai, Ligeng Zhu, and Song Han · 2018
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Pitfalls of graph neural network evaluation
Neural architecture search in graph neural networks
Matheus Nunes et al · 2020
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Autograph: Automated graph neural network
Yaoman Li and Irwin King · 2020
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Evolutionary architecture search for graph neural networks
Min Shi et al · 2020
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Graph neural network architecture search for molecular property prediction
Shengli Jiang and Prasanna Balaprakash · 2020
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Learned low precision graph neural networks
Yiren Zhao et al · 2020
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Design space for graph neural networks
Jiaxuan You, Zhitao Ying, and Jure Leskovec · 2020
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Oleksandr Shchur et al · 2018
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Learning disentangled representations for recommendation
Jianxin Ma et al · 2019
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Automated machine learning: State-of-the-art and open challenges
Radwa Elshawi, Mohamed Maher, and Sherif Sakr · 2019
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Neural architecture search: A survey
Thomas Elsken et al · 2019
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Regularized evolution for image classifier architecture search
Esteban Real et al · 2019
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Autone: Hyperparameter optimization for massive network embedding
Ke Tu et al · 2019
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Auto-gnn: Neural architecture search of graph neural networks
Kaixiong Zhou et al · 2019
Cited alongside, same era.
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Sgas: Sequential greedy architecture search
Guohao Li et al · 2020
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Learning graph convolutional network for skeleton-based human action recognition by neural searching
Wei Peng et al · 2020
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Bridging the gap between sample-based and one-shot neural architecture search with bonas
Han Shi et al · 2020
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Single path one-shot neural architecture search with uniform sampling
Zichao Guo et al · 2020
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Graph structure of neural networks
Jiaxuan You et al · 2020
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Graph neural networks in tensorflow and keras with spektral
Daniele Grattarola and Cesare Alippi · 2020
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Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs
Benedek Rozemberczki, Oliver Kiss, and Rik Sarkar · 2020
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Retiarii: A deep learning exploratory-training framework
Quanlu Zhang et al · 2020
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Autogluon-tabular: Robust and accurate automl for structured data
Nick Erickson et al · 2020
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Brp-nas: Prediction-based nas using gcns
Lukasz Dudziak et al · 2020
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Explainability in graph neural networks: A taxonomic survey
Hao Yuan et al · 2020
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Hardware acceleration of graph neural networks
Adam Auten, Matthew Tomei, and Rakesh Kumar · 2020
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Hardware-aware transformable architecture search with efficient search space
Yuhang Jiang, Xin Wang, and Wenwu Zhu · 2020
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Benchmarking graph neural networks
Vijay Prakash Dwivedi et al · 2020
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Jitune: Just-in-time hyperparameter tuning for network embedding algorithms
Mengying Guo et al · 2021
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Yingfang Yuan et al · 2021
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Efficient graph neural architecture search
Huan Zhao et al · 2021
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Rethinking graph neural network search from message-passing
Shaofei Cai et al · 2021
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Autostg: Neural architecture search for predictions of spatio-temporal graphs
Zheyi Pan et al · 2021
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One-shot graph neural architecture search with dynamic search space
Yanxi Li et al · 2021
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Search to aggregate neighborhood for graph neural network
Huan Zhao, Quanming Yao, and Weiwei Tu · 2021
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Autoattend: Automated attention representation search
Chaoyu Guan, Xin Wang, and Wenwu Zhu · 2021
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Euler: A distributed graph deep learning framework
Alibaba · 2021
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Stellargraph machine learning library
CSIRO’s Data61 · 2021
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Cogdl: An extensive toolkit for deep learning on graphs
Yukuo Cen et al · 2021
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Openne: An open source toolkit for network embedding
Tsinghua University Natural Language Processing Lab · 2021
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Dig: A turnkey library for diving into graph deep learning research
Meng Liu et al · 2021
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Mlbox, machine learning box
Axel de Romblay et al · 2021
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Mljar automated machine learning
MLJAR · 2021
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AutoGL: A library for automated graph learning
Chaoyu Guan et al · 2021
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Explainable automated graph representation learning with hyperparameter importance
Xin Wang et al · 2021
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