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Graph neural architecture search. In IJCAI. 1403--1409. Yang Gao Hong Yang Peng Zhang Chuan Zhou and Yue Hu. 2021a. Graph neural architecture search. In IJCAI. 1403--1409.","DOI":"10.1109\/ICDM51629.2021.00124"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-017-1016-8"},{"key":"e_1_3_2_1_8_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1024--1034. Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1024--1034."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"crossref","unstructured":"Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural collaborative filtering. In TheWebConf. 173--182. Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural collaborative filtering. In TheWebConf. 173--182.","DOI":"10.1145\/3038912.3052569"},{"key":"e_1_3_2_1_11_1","first-page":"2","article-title":"Neural networks for machine learning lecture 6a overview of mini-batch gradient descent","volume":"14","author":"Hinton Geoffrey","year":"2012","unstructured":"Geoffrey Hinton , Nitish Srivastava , and Kevin Swersky . 2012 . Neural networks for machine learning lecture 6a overview of mini-batch gradient descent . Cited on , Vol. 14 (2012), 2 . Geoffrey Hinton, Nitish Srivastava, and Kevin Swersky. 2012. Neural networks for machine learning lecture 6a overview of mini-batch gradient descent. Cited on, Vol. 14 (2012), 2.","journal-title":"Cited on"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/359138.359141"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"ZHAO Huan YAO Quanming and TU Weiwei. 2021. Search to aggregate neighborhood for graph neural network. In ICDE. 552--563. ZHAO Huan YAO Quanming and TU Weiwei. 2021. Search to aggregate neighborhood for graph neural network. In ICDE. 552--563.","DOI":"10.1109\/ICDE51399.2021.00054"},{"key":"e_1_3_2_1_14_1","unstructured":"Wonyong Jeong Hayeon Lee Geon Park Eunyoung Hyung Jinheon Baek and Sung Ju Hwang. 2021. Task-Adaptive Neural Network Search with Meta-Contrastive Learning. In NeurIPS. 21310--21324. Wonyong Jeong Hayeon Lee Geon Park Eunyoung Hyung Jinheon Baek and Sung Ju Hwang. 2021. Task-Adaptive Neural Network Search with Meta-Contrastive Learning. In NeurIPS. 21310--21324."},{"key":"e_1_3_2_1_15_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. 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Dongze Lian Yin Zheng Yintao Xu Yanxiong Lu Leyu Lin Peilin Zhao Junzhou Huang and Shenghua Gao. 2019. Towards fast adaptation of neural architectures with meta learning. In ICLR."},{"key":"e_1_3_2_1_22_1","volume-title":"Darts: Differentiable architecture search. In ICLR.","author":"Liu Hanxiao","year":"2019","unstructured":"Hanxiao Liu , Karen Simonyan , and Yiming Yang . 2019 . Darts: Differentiable architecture search. In ICLR. Hanxiao Liu, Karen Simonyan, and Yiming Yang. 2019. Darts: Differentiable architecture search. In ICLR."},{"key":"e_1_3_2_1_23_1","volume-title":"Foundations and Trends\u00ae in Information Retrieval","volume":"3","author":"Tie-Yan","year":"2009","unstructured":"Tie-Yan Liu et al. 2009. Learning to rank for information retrieval . Foundations and Trends\u00ae in Information Retrieval , Vol. 3 ( 2009 ), 225--331. Tie-Yan Liu et al. 2009. Learning to rank for information retrieval. Foundations and Trends\u00ae in Information Retrieval, Vol. 3 (2009), 225--331."},{"key":"e_1_3_2_1_24_1","unstructured":"Yaoqi Liu Cheng Yang Tianyu Zhao Hui Han Siyuan Zhang Jing Wu Guangyu Zhou Hai Huang Hui Wang and Chuan Shi. [n. d.]. GammaGL: A Multi-Backend Library for Graph Neural Networks. In SIGIR. Yaoqi Liu Cheng Yang Tianyu Zhao Hui Han Siyuan Zhang Jing Wu Guangyu Zhou Hai Huang Hui Wang and Chuan Shi. [n. d.]. GammaGL: A Multi-Backend Library for Graph Neural Networks. In SIGIR."},{"key":"e_1_3_2_1_25_1","volume-title":"Neural models for information retrieval. arXiv preprint arXiv:1705.01509","author":"Mitra Bhaskar","year":"2017","unstructured":"Bhaskar Mitra and Nick Craswell . 2017. Neural models for information retrieval. arXiv preprint arXiv:1705.01509 ( 2017 ). Bhaskar Mitra and Nick Craswell. 2017. Neural models for information retrieval. arXiv preprint arXiv:1705.01509 (2017)."},{"key":"e_1_3_2_1_26_1","volume-title":"Mixing patterns in networks. 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BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"crossref","unstructured":"Steffen Rendle Walid Krichene Li Zhang and John Anderson. 2020. Neural collaborative filtering vs. matrix factorization revisited. In RecSys. 240--248. Steffen Rendle Walid Krichene Li Zhang and John Anderson. 2020. Neural collaborative filtering vs. matrix factorization revisited. In RecSys. 240--248.","DOI":"10.1145\/3383313.3412488"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Badrul Sarwar George Karypis Joseph Konstan and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In TheWeConf. 285--295. Badrul Sarwar George Karypis Joseph Konstan and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In TheWeConf. 285--295.","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1213\/ANE.0000000000002864"},{"key":"e_1_3_2_1_36_1","unstructured":"Gresa Shala Thomas Elsken Frank Hutter and Josif Grabocka. 2023. Transfer NAS with Meta-learned Bayesian Surrogates. In ICLR. Gresa Shala Thomas Elsken Frank Hutter and Josif Grabocka. 2023. Transfer NAS with Meta-learned Bayesian Surrogates. In ICLR."},{"key":"e_1_3_2_1_37_1","unstructured":"Albert Shaw Wei Wei Weiyang Liu Le Song and Bo Dai. 2019. Meta architecture search. In NeurIPS. 11225--11235. Albert Shaw Wei Wei Weiyang Liu Le Song and Bo Dai. 2019. Meta architecture search. In NeurIPS. 11225--11235."},{"key":"e_1_3_2_1_38_1","unstructured":"Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR. Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR."},{"key":"e_1_3_2_1_39_1","unstructured":"Vikas Verma Alex Lamb Christopher Beckham Amir Najafi Ioannis Mitliagkas David Lopez-Paz and Yoshua Bengio. 2019. Manifold mixup: Better representations by interpolating hidden states. In ICML. 6438--6447. Vikas Verma Alex Lamb Christopher Beckham Amir Najafi Ioannis Mitliagkas David Lopez-Paz and Yoshua Bengio. 2019. Manifold mixup: Better representations by interpolating hidden states. In ICML. 6438--6447."},{"key":"e_1_3_2_1_40_1","volume-title":"A perspective view and survey of meta-learning. Artificial intelligence review","author":"Vilalta Ricardo","year":"2002","unstructured":"Ricardo Vilalta and Youssef Drissi . 2002. A perspective view and survey of meta-learning. Artificial intelligence review , Vol. 18 , 2 ( 2002 ), 77--95. Ricardo Vilalta and Youssef Drissi. 2002. A perspective view and survey of meta-learning. Artificial intelligence review, Vol. 18, 2 (2002), 77--95."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174. Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174.","DOI":"10.1145\/3331184.3331267"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Xiang Wang Hongye Jin An Zhang Xiangnan He Tong Xu and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In SIGIR. 1001--1010. Xiang Wang Hongye Jin An Zhang Xiangnan He Tong Xu and Tat-Seng Chua. 2020. Disentangled Graph Collaborative Filtering. In SIGIR. 1001--1010.","DOI":"10.1145\/3397271.3401137"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Zhenyi Wang Huan Zhao and Chuan Shi. 2022. Profiling the Design Space for Graph Neural Networks Based Collaborative Filtering. In WSDM. 1109--1119. Zhenyi Wang Huan Zhao and Chuan Shi. 2022. Profiling the Design Space for Graph Neural Networks Based Collaborative Filtering. In WSDM. 1109--1119.","DOI":"10.1145\/3488560.3498520"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Lanning Wei Zhiqiang He Huan Zhao and Quanming Yao. 2023. Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification. In TheWebConf. 588--598. Lanning Wei Zhiqiang He Huan Zhao and Quanming Yao. 2023. Search to Capture Long-range Dependency with Stacking GNNs for Graph Classification. In TheWebConf. 588--598.","DOI":"10.1145\/3543507.3583486"},{"key":"e_1_3_2_1_45_1","unstructured":"Lanning Wei Huan Zhao and Zhiqiang He. 2022. Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective. In TheWebConf. 1381--1391. Lanning Wei Huan Zhao and Zhiqiang He. 2022. Designing the Topology of Graph Neural Networks: A Novel Feature Fusion Perspective. In TheWebConf. 1381--1391."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Lanning Wei Huan Zhao Quanming Yao and Zhiqiang He. 2021. Pooling architecture search for graph classification. In CIKM. 2091--2100. Lanning Wei Huan Zhao Quanming Yao and Zhiqiang He. 2021. Pooling architecture search for graph classification. In CIKM. 2091--2100.","DOI":"10.1145\/3459637.3482285"},{"key":"e_1_3_2_1_47_1","volume-title":"Graph neural networks in recommender systems: a survey. ACM Computing Surveys (CSUR)","author":"Wu Shiwen","year":"2020","unstructured":"Shiwen Wu , Fei Sun , Wentao Zhang , Xu Xie , and Bin Cui . 2020. Graph neural networks in recommender systems: a survey. ACM Computing Surveys (CSUR) ( 2020 ). Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2020. Graph neural networks in recommender systems: a survey. ACM Computing Surveys (CSUR) (2020)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"crossref","unstructured":"Fen Xia Tie-Yan Liu Jue Wang Wensheng Zhang and Hang Li. 2008. Listwise approach to learning to rank: theory and algorithm. In ICML. 1192--1199. Fen Xia Tie-Yan Liu Jue Wang Wensheng Zhang and Hang Li. 2008. Listwise approach to learning to rank: theory and algorithm. In ICML. 1192--1199.","DOI":"10.1145\/1390156.1390306"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Yongqin Xian Bernt Schiele and Zeynep Akata. 2017. Zero-shot learning-the good the bad and the ugly. In CVPR. 4582--4591. Yongqin Xian Bernt Schiele and Zeynep Akata. 2017. Zero-shot learning-the good the bad and the ugly. In CVPR. 4582--4591.","DOI":"10.1109\/CVPR.2017.328"},{"key":"e_1_3_2_1_50_1","unstructured":"Lee Xiong Chenyan Xiong Ye Li Kwok-Fung Tang Jialin Liu Paul Bennett Junaid Ahmed and Arnold Overwijk. 2021. Approximate nearest neighbor negative contrastive learning for dense text retrieval. In ICLR. Lee Xiong Chenyan Xiong Ye Li Kwok-Fung Tang Jialin Liu Paul Bennett Junaid Ahmed and Arnold Overwijk. 2021. Approximate nearest neighbor negative contrastive learning for dense text retrieval. In ICLR."},{"key":"e_1_3_2_1_51_1","unstructured":"Huaxiu Yao Linjun Zhang and Chelsea Finn. 2021. Meta-Learning with Fewer Tasks through Task Interpolation. In ICLR. Huaxiu Yao Linjun Zhang and Chelsea Finn. 2021. Meta-Learning with Fewer Tasks through Task Interpolation. In ICLR."},{"key":"e_1_3_2_1_52_1","doi-asserted-by":"crossref","unstructured":"Quanming Yao Xiangning Chen James T Kwok Yong Li and Cho-Jui Hsieh. 2020. Efficient neural interaction function search for collaborative filtering. In TheWebConf. 1660--1670. Quanming Yao Xiangning Chen James T Kwok Yong Li and Cho-Jui Hsieh. 2020. Efficient neural interaction function search for collaborative filtering. In TheWebConf. 1660--1670.","DOI":"10.1145\/3366423.3380237"},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"crossref","unstructured":"Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William L Hamilton and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In KDD. 974--983. Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William L Hamilton and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In KDD. 974--983.","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_54_1","unstructured":"Jiaxuan You Zhitao Ying and Jure Leskovec. 2020. Design space for graph neural networks. In NeurIPS. 17009--17021. Jiaxuan You Zhitao Ying and Jure Leskovec. 2020. Design space for graph neural networks. In NeurIPS. 17009--17021."},{"key":"e_1_3_2_1_55_1","unstructured":"Manzil Zaheer Satwik Kottur Siamak Ravanbakhsh Barnabas Poczos Russ R Salakhutdinov and Alexander J Smola. 2017. Deep sets. In NeurIPS. 3391--3401. Manzil Zaheer Satwik Kottur Siamak Ravanbakhsh Barnabas Poczos Russ R Salakhutdinov and Alexander J Smola. 2017. Deep sets. In NeurIPS. 3391--3401."},{"key":"e_1_3_2_1_56_1","volume-title":"KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning. arXiv preprint arXiv:2205.02460","author":"Zhang Yongqi","year":"2022","unstructured":"Yongqi Zhang , Zhanke Zhou , Quanming Yao , and Yong Li. 2022. KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning. arXiv preprint arXiv:2205.02460 ( 2022 ). Yongqi Zhang, Zhanke Zhou, Quanming Yao, and Yong Li. 2022. KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning. arXiv preprint arXiv:2205.02460 (2022)."},{"key":"e_1_3_2_1_57_1","doi-asserted-by":"crossref","unstructured":"Tianyu Zhao Cheng Yang Yibo Li Quan Gan Zhenyi Wang Fengqi Liang Huan Zhao Yingxia Shao Xiao Wang and Chuan Shi. 2022b. Space4hgnn: a novel modularized and reproducible platform to evaluate heterogeneous graph neural network. In SIGIR. 2776--2789. Tianyu Zhao Cheng Yang Yibo Li Quan Gan Zhenyi Wang Fengqi Liang Huan Zhao Yingxia Shao Xiao Wang and Chuan Shi. 2022b. Space4hgnn: a novel modularized and reproducible platform to evaluate heterogeneous graph neural network. In SIGIR. 2776--2789.","DOI":"10.1145\/3477495.3531720"},{"key":"e_1_3_2_1_58_1","unstructured":"Wayne Xin Zhao Yupeng Hou Xingyu Pan Chen Yang Zeyu Zhang Zihan Lin Jingsen Zhang Shuqing Bian Jiakai Tang Wenqi Sun etal 2022a. RecBole 2.0: Towards a More Up-to-Date Recommendation Library. arXiv preprint arXiv:2206.07351 (2022). Wayne Xin Zhao Yupeng Hou Xingyu Pan Chen Yang Zeyu Zhang Zihan Lin Jingsen Zhang Shuqing Bian Jiakai Tang Wenqi Sun et al. 2022a. RecBole 2.0: Towards a More Up-to-Date Recommendation Library. arXiv preprint arXiv:2206.07351 (2022)."},{"key":"e_1_3_2_1_59_1","volume-title":"Autoloss: Automated loss function search in recommendations. 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Learning transferable architectures for scalable image recognition. 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