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Graph machine learning has been extensively studied in both academia and industry.
Markov decision processes
Martin L Puterman · 1990
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Learning and development in neural networks: The importance of starting small
Jeffrey L Elman · 1993
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Approximation algorithms for np-hard problems
Dorit S Hochba · 1997
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Language acquisition in the absence of explicit negative evidence: How important is starting small?
Douglas LT Rohde and David C Plaut · 1999
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Ranking-based clustering of heterogeneous information networks with star network schema
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Importance sampling: a review
Surya T Tokdar and Robert E Kass · 2010
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The traveling salesman problem
David L Applegate, Robert E Bixby, Vašek Chvátal, and William J Cook · 2011
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Pathsim: Meta path-based top-k similarity search in heterogeneous information networks
Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu · 2011
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Graph analysis of functional brain networks: practical issues in translational neuroscience
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Easy samples first: Self-paced reranking for zero-example multimedia search
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Time series analysis: forecasting and control
George EP Box, Gwilym M Jenkins, Gregory C Reinsel, and Greta M Ljung · 2015
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Why curriculum learning & self-paced learning work in big/noisy data: A theoretical perspective
Tieliang Gong, Qian Zhao, Deyu Meng, and Zongben Xu · 2016
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Final: Fast attributed network alignment
Si Zhang and Hanghang Tong · 2016
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Event classification in microblogs via social tracking
Yue Gao, Hanwang Zhang, Xibin Zhao, and Shuicheng Yan · 2017
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Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl · 2017
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Knowledge graph embedding: A survey of approaches and applications
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo · 2017
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Community preserving network embedding
Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang · 2017
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A survey on network embedding
Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu · 2018
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Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann · 2018
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Curriculumnet: Weakly supervised learning from large-scale web images
Sheng Guo, Weilin Huang, Haozhi Zhang, Chenfan Zhuang, Dengke Dong, Matthew R Scott, and Dinglong Huang · 2018
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Efficient neural architecture search via parameters sharing
Hieu Pham, Melody Guan, Barret Zoph, Quoc Le, and Jeff Dean · 2018
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Curriculum learning for heterogeneous star network embedding via deep reinforcement learning
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Graph attention networks
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Curriculum learning by transfer learning: Theory and experiments with deep networks
Daphna Weinshall, Gad Cohen, and Dan Amir · 2018
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Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
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Multi-modal curriculum learning over graphs
Chen Gong, Jian Yang, and Dacheng Tao · 2019
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On the power of curriculum learning in training deep networks
Guy Hacohen and Daphna Weinshall · 2019
Drug repurposing for covid-19 using graph neural network and harmonizing multiple evidence
Kanglin Hsieh, Yinyin Wang, Luyao Chen, Zhongming Zhao, Sean Savitz, Xiaoqian Jiang, Jing Tang, and Yejin Kim · 2021
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Curriculum graph co-teaching for multi-target domain adaptation
Subhankar Roy, Evgeny Krivosheev, Zhun Zhong, Nicu Sebe, and Elisa Ricci · 2021
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Directed graph contrastive learning
Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, and Changhu Wang · 2021
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A survey on curriculum learning
Xin Wang, Yudong Chen, and Wenwu Zhu · 2021
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Curgraph: Curriculum learning for graph classification
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Shift-robust gnns: Overcoming the limitations of localized graph training data
Qi Zhu, Natalia Ponomareva, Jiawei Han, and Bryan Perozzi · 2021
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Label propagation for deep semi-supervised learning
Ahmet Iscen, Giorgos Tolias, Yannis Avrithis, and Ondrej Chum · 2019
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Competence-based curriculum learning for neural machine translation
Emmanouil Antonios Platanios, Otilia Stretcu, Graham Neubig, Barnabas Poczos, and Tom M Mitchell · 2019
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Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization
Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang · 2019
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Yi Tay, Shuohang Wang, Luu Anh Tuan, Jie Fu, Minh C Phan, Xingdi Yuan, Jinfeng Rao, Siu Cheung Hui, and Aston Zhang · 2019
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Dynamic curriculum learning for imbalanced data classification
Yiru Wang, Weihao Gan, Jie Yang, Wei Wu, and Junjie Yan · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Hyper-graph-based attention curriculum learning using a lexical algorithm for mental health
Usman Ahmed, Jerry Chun-Wei Lin, and Gautam Srivastava · 2022
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An efficient curriculum learning-based strategy for molecular graph learning
Yaowen Gu, Si Zheng, Zidu Xu, Qijin Yin, Liang Li, and Jiao Li · 2022
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Large-scale graph neural architecture search
Chaoyu Guan, Xin Wang, Hong Chen, Ziwei Zhang, and Wenwu Zhu · 2022
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Tuneup: A training strategy for improving generalization of graph neural networks
Weihua Hu, Kaidi Cao, Kexin Huang, Edward W Huang, Karthik Subbian, and Jure Leskovec · 2022
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Ood-gnn: Out-of-distribution generalized graph neural network
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Out-of-distribution generalization on graphs: A survey
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Graph neural network with curriculum learning for imbalanced node classification
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Curriculum learning: A survey
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Generic and trend-aware curriculum learning for relation extraction in graph neural networks
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Clnode: Curriculum learning for node classification
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Graph neural networks in recommender systems: a survey
Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui · 2022
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Few-shot learning on graphs: A survey
Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V Chawla, and Huan Liu · 2022
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Learning to solve travelling salesman problem with hardness-adaptive curriculum
Zeyang Zhang, Ziwei Zhang, Xin Wang, and Wenwu Zhu · 2022
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Mentorgnn: deriving curriculum for pre-training gnns
Dawei Zhou, Lecheng Zheng, Dongqi Fu, Jiawei Han, and Jingrui He · 2022
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Curml: A curriculum machine learning library
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Hard sample aware network for contrastive deep graph clustering
Yue Liu, Xihong Yang, Sihang Zhou, Xinwang Liu, Zhen Wang, Ke Liang, Wenxuan Tu, Liang Li, Jingcan Duan, and Cancan Chen · 2023
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Curriculum pre-training heterogeneous subgraph transformer for top-n recommendation
Hui Wang, Kun Zhou, Xin Zhao, Jingyuan Wang, and Ji-Rong Wen · 2023
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Curriculum learning for graph neural networks: Which edges should we learn first
Zheng Zhang, Junxiang Wang, and Liang Zhao · 2023
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