{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T03:08:11Z","timestamp":1784603291660,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,7,11]],"date-time":"2021-07-11T00:00:00Z","timestamp":1625961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,7,11]]},"DOI":"10.1145\/3404835.3463056","type":"proceedings-article","created":{"date-parts":[[2021,7,12]],"date-time":"2021-07-12T02:41:48Z","timestamp":1626057708000},"page":"2096-2100","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["Automated Graph Learning via Population Based Self-Tuning GCN"],"prefix":"10.1145","author":[{"given":"Ronghang","family":"Zhu","sequence":"first","affiliation":[{"name":"University of Georgia, Athens, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Tao","sequence":"additional","affiliation":[{"name":"Santa Clara University, Santa Clara, CA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaliang","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Bellevue, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng","family":"Li","sequence":"additional","affiliation":[{"name":"University of Georgia, Athens, GA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,7,11]]},"reference":[{"key":"e_1_3_2_2_1_1","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra James","year":"2012","unstructured":"James Bergstra and Yoshua Bengio. 2012. Random search for hyper-parameter optimization. Journal of machine learning research, Vol. 13, 2 (2012).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Smriti Bhagat Graham Cormode and S Muthukrishnan. 2011. Node classification in social networks. In Social network data analytics. 115--148.","DOI":"10.1007\/978-1-4419-8462-3_5"},{"key":"e_1_3_2_2_3_1","unstructured":"Joan Bruna Wojciech Zaremba Arthur Szlam and Yann Lecun. 2014. Spectral networks and locally connected networks on graphs. In ICLR."},{"key":"e_1_3_2_2_4_1","unstructured":"Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and deep graph convolutional networks. In ICML."},{"key":"e_1_3_2_2_5_1","volume-title":"BOHB: Robust and Efficient Hyperparameter Optimization at Scale. In ICML.","author":"Falkner Stefan","year":"2018","unstructured":"Stefan Falkner, Aaron Klein, and Frank Hutter. 2018. BOHB: Robust and Efficient Hyperparameter Optimization at Scale. In ICML."},{"key":"e_1_3_2_2_6_1","volume-title":"Hyperparameter Optimization","author":"Feurer Matthias","unstructured":"Matthias Feurer and Frank Hutter. 2019. Hyperparameter Optimization. Springer International Publishing, 3--33."},{"key":"e_1_3_2_2_7_1","unstructured":"Luca Franceschi Michele Donini Paolo Frasconi and Massimiliano Pontil. 2017. Forward and Reverse Gradient-Based Hyperparameter Optimization. In ICML."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"Yang Gao Hong Yang Peng Zhang Chuan Zhou and Yue Hu. 2020. Graph Neural Architecture Search. In IJCAI.","DOI":"10.24963\/ijcai.2020\/195"},{"key":"e_1_3_2_2_9_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NeurIPS."},{"key":"e_1_3_2_2_10_1","unstructured":"Max Jaderberg Valentin Dalibard Simon Osindero Wojciech M Czarnecki Jeff Donahue Ali Razavi Oriol Vinyals Tim Green Iain Dunning Karen Simonyan et al. 2017. Population based training of neural networks. arXiv preprint arXiv:1711.09846 (2017)."},{"key":"e_1_3_2_2_11_1","volume-title":"Graph Neural Network Architecture Search for Molecular Property Prediction. arXiv preprint arXiv:2008.12187","author":"Jiang Shengli","year":"2020","unstructured":"Shengli Jiang and Prasanna Balaprakash. 2020. Graph Neural Network Architecture Search for Molecular Property Prediction. arXiv preprint arXiv:2008.12187 (2020)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Xiaodong Jiang Pengsheng Ji and Sheng Li. 2019. CensNet: Convolution with Edge-Node Switching in Graph Neural Networks. In IJCAI.","DOI":"10.24963\/ijcai.2019\/369"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3029762"},{"key":"e_1_3_2_2_14_1","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_2_15_1","unstructured":"Kwei-Herng Lai Daochen Zha Kaixiong Zhou and Xia Hu. 2020. Policy-GNN: Aggregation Optimization for Graph Neural Networks. In SIGKDD."},{"key":"e_1_3_2_2_16_1","volume-title":"Deepgcns: Can gcns go as deep as cnns?. In CVPR.","author":"Li Guohao","year":"2019","unstructured":"Guohao Li, Matthias Muller, Ali Thabet, and Bernard Ghanem. 2019. Deepgcns: Can gcns go as deep as cnns?. In CVPR."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.5555\/3122009.3242042"},{"key":"e_1_3_2_2_18_1","unstructured":"Matthew MacKay Paul Vicol Jon Lorraine David Duvenaud and Roger Grosse. 2019. Self-tuning networks: Bilevel optimization of hyperparameters using structured best-response functions. In ICLR."},{"key":"e_1_3_2_2_19_1","volume-title":"Scikit-learn: Machine learning in Python. the Journal of machine Learning research","author":"Pedregosa Fabian","year":"2011","unstructured":"Fabian Pedregosa, Ga\u00ebl Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. 2011. Scikit-learn: Machine learning in Python. the Journal of machine Learning research, Vol. 12 (2011), 2825--2830."},{"key":"e_1_3_2_2_20_1","volume-title":"EDGE: Enriching Knowledge Graph Embeddings with External Text. In NAACL.","author":"Rezayi Saed","year":"2021","unstructured":"Saed Rezayi, Handong Zhao, Sungchul Kim, Ryan Rossi, Nedim Lipka, and Sheng Li. 2021. EDGE: Enriching Knowledge Graph Embeddings with External Text. In NAACL."},{"key":"e_1_3_2_2_21_1","volume-title":"Dropedge: Towards deep graph convolutional networks on node classification. In ICLR.","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. Dropedge: Towards deep graph convolutional networks on node classification. In ICLR."},{"key":"e_1_3_2_2_22_1","volume-title":"Collective classification in network data. AI magazine","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine, Vol. 29, 3 (2008), 93--93."},{"key":"e_1_3_2_2_23_1","unstructured":"Heng-Shiou Sheu and Sheng Li. 2020. Context-aware graph embedding for session-based news recommendation. In ACM RecSys."},{"key":"e_1_3_2_2_24_1","volume-title":"Evolutionary Architecture Search for Graph Neural Networks. arXiv preprint arXiv:2009.10199","author":"Shi Min","year":"2020","unstructured":"Min Shi, David A. Wilson, Xingquan Zhu, Yu Huang, Yuan Zhuang, Jianxun Liu, and Yufei Tang. 2020. Evolutionary Architecture Search for Graph Neural Networks. arXiv preprint arXiv:2009.10199 (2020)."},{"key":"e_1_3_2_2_25_1","unstructured":"Zhiqiang Tao Yaliang Li Bolin Ding Ce Zhang Jingren Zhou and Yun Fu. 2020. Learning to Mutate with Hypergradient Guided Population. In NeurIPS."},{"key":"e_1_3_2_2_26_1","unstructured":"Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_2_27_1","volume-title":"Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434","author":"Zhou Jie","year":"2018","unstructured":"Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Lifeng Wang, Changcheng Li, and Maosong Sun. 2018. Graph neural networks: A review of methods and applications. arXiv preprint arXiv:1812.08434 (2018)."},{"key":"e_1_3_2_2_28_1","volume-title":"Auto-GNN: Neural Architecture Search of Graph Neural Networks. arXiv preprint arXiv:1909.03184","author":"Zhou Kaixiong","year":"2019","unstructured":"Kaixiong Zhou, Qingquan Song, Xiao Huang, and Xia Hu. 2019. Auto-GNN: Neural Architecture Search of Graph Neural Networks. arXiv preprint arXiv:1909.03184 (2019)."},{"key":"e_1_3_2_2_29_1","unstructured":"Ronghang Zhu Xiaodong Jiang Jiasen Lu and Sheng Li. 2021. Transferable Feature Learning on Graphs Across Visual Domains. In ICME."}],"event":{"name":"SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Virtual Event Canada","acronym":"SIGIR '21","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404835.3463056","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3404835.3463056","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:30Z","timestamp":1750191510000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3404835.3463056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,11]]},"references-count":29,"alternative-id":["10.1145\/3404835.3463056","10.1145\/3404835"],"URL":"https:\/\/doi.org\/10.1145\/3404835.3463056","relation":{},"subject":[],"published":{"date-parts":[[2021,7,11]]},"assertion":[{"value":"2021-07-11","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}