{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T02:13:23Z","timestamp":1788920003153,"version":"build-2803163510"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20B2045, 61772082, 61702296, 62002029"],"award-info":[{"award-number":["U20B2045, 61772082, 61702296, 62002029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Keyfields RD project Of Guangdong Province","award":["2020B0101380001"],"award-info":[{"award-number":["2020B0101380001"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467396","type":"proceedings-article","created":{"date-parts":[[2021,8,12]],"date-time":"2021-08-12T06:13:10Z","timestamp":1628748790000},"page":"756-766","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Pre-training on Large-Scale Heterogeneous Graph"],"prefix":"10.1145","author":[{"given":"Xunqiang","family":"Jiang","sequence":"first","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tianrui","family":"Jia","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuan","family":"Fang","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuan","family":"Shi","sequence":"additional","affiliation":[{"name":"Beijing University of Posts and Telecommunications & Peng Cheng Laboratory, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Lin","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Aleksandar Bojchevski Johannes Klicpera Bryan Perozzi Amol Kapoor Martin Blais Benedek R\u00f3zemberczki Michal Lukasik and Stephan G\u00fcnnemann. 2020. Scaling graph neural networks with approximate pagerank. In SIGKDD. 2464--2473.  Aleksandar Bojchevski Johannes Klicpera Bryan Perozzi Amol Kapoor Martin Blais Benedek R\u00f3zemberczki Michal Lukasik and Stephan G\u00fcnnemann. 2020. Scaling graph neural networks with approximate pagerank. In SIGKDD. 2464--2473.","DOI":"10.1145\/3394486.3403296"},{"key":"e_1_3_2_2_2_1","unstructured":"Joan Bruna Wojciech Zaremba Arthur Szlam and Yann LeCun. 2014. Spectral Networks and Locally Connected Networks on Graphs. In ICLR.  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_3_1","unstructured":"Julian Busch Jiaxing Pi and Thomas Seidl. 2020. PushNet: Efficient and Adaptive Neural Message Passing. ECAI.  Julian Busch Jiaxing Pi and Thomas Seidl. 2020. PushNet: Efficient and Adaptive Neural Message Passing. ECAI."},{"key":"e_1_3_2_2_4_1","unstructured":"Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML. PMLR 1597--1607.  Ting Chen Simon Kornblith Mohammad Norouzi and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In ICML. PMLR 1597--1607."},{"key":"e_1_3_2_2_5_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In NIPS. 3837--3845.  Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In NIPS. 3837--3845."},{"key":"e_1_3_2_2_6_1","volume-title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. 4171--4186.","author":"Devlin Jacob","year":"2019","unstructured":"Jacob Devlin , Ming-Wei Chang , Kenton Lee , and Kristina Toutanova . 2019 . BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. 4171--4186. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In NAACL-HLT. 4171--4186."},{"key":"e_1_3_2_2_7_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop."},{"key":"e_1_3_2_2_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_2_9_1","unstructured":"Kaiming He Haoqi Fan Yuxin Wu Saining Xie and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In CVPR. 9729--9738.  Kaiming He Haoqi Fan Yuxin Wu Saining Xie and Ross Girshick. 2020. Momentum contrast for unsupervised visual representation learning. In CVPR. 9729--9738."},{"key":"e_1_3_2_2_10_1","unstructured":"Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay S. Pande and Jure Leskovec. 2020 c. Strategies for Pre-training Graph Neural Networks. In ICLR.  Weihua Hu Bowen Liu Joseph Gomes Marinka Zitnik Percy Liang Vijay S. Pande and Jure Leskovec. 2020 c. Strategies for Pre-training Graph Neural Networks. In ICLR."},{"key":"e_1_3_2_2_11_1","unstructured":"Ziniu Hu Yuxiao Dong Kuansan Wang Kai-Wei Chang and Yizhou Sun. 2020 b. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In SIGKDD. 1857--1867.  Ziniu Hu Yuxiao Dong Kuansan Wang Kai-Wei Chang and Yizhou Sun. 2020 b. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In SIGKDD. 1857--1867."},{"key":"e_1_3_2_2_12_1","unstructured":"Ziniu Hu Yuxiao Dong Kuansan Wang and Yizhou Sun. 2020 a. Heterogeneous Graph Transformer. In WWW. 2704--2710.  Ziniu Hu Yuxiao Dong Kuansan Wang and Yizhou Sun. 2020 a. Heterogeneous Graph Transformer. In WWW. 2704--2710."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Glen Jeh and Jennifer Widom. 2003. Scaling personalized web search. In WWW. 271--279.  Glen Jeh and Jennifer Widom. 2003. Scaling personalized web search. In WWW. 271--279.","DOI":"10.1145\/775152.775191"},{"key":"e_1_3_2_2_14_1","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. In ICLR.  Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_2_15_1","volume-title":"ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR.","author":"Lan Zhenzhong","year":"2020","unstructured":"Zhenzhong Lan , Mingda Chen , Sebastian Goodman , Kevin Gimpel , Piyush Sharma , and Radu Soricut . 2020 . ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR. Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In ICLR."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"crossref","unstructured":"Jure Leskovec and Christos Faloutsos. 2006. Sampling from large graphs. In SIGKDD. 631--636.  Jure Leskovec and Christos Faloutsos. 2006. Sampling from large graphs. In SIGKDD. 631--636.","DOI":"10.1145\/1150402.1150479"},{"key":"e_1_3_2_2_17_1","unstructured":"Yujia Li Oriol Vinyals Chris Dyer Razvan Pascanu and Peter W Battaglia. 2018. Learning Deep Generative Models of Graphs. In ICLR.  Yujia Li Oriol Vinyals Chris Dyer Razvan Pascanu and Peter W Battaglia. 2018. Learning Deep Generative Models of Graphs. In ICLR."},{"key":"e_1_3_2_2_18_1","unstructured":"Tie-Yan Liu. 2011. Learning to rank for information retrieval. (2011).  Tie-Yan Liu. 2011. Learning to rank for information retrieval. (2011)."},{"key":"e_1_3_2_2_19_1","volume-title":"Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983","author":"Loshchilov Ilya","year":"2016","unstructured":"Ilya Loshchilov and Frank Hutter . 2016 . Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016). Ilya Loshchilov and Frank Hutter. 2016. Sgdr: Stochastic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983 (2016)."},{"key":"e_1_3_2_2_20_1","volume-title":"Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101","author":"Loshchilov Ilya","year":"2017","unstructured":"Ilya Loshchilov and Frank Hutter . 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 ( 2017 ). Ilya Loshchilov and Frank Hutter. 2017. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)."},{"key":"e_1_3_2_2_21_1","volume-title":"Proceedings of IJCNN. 729--734","author":"Marco Gori","year":"2005","unstructured":"Gori Marco , Monfardini Gabriele , and Scarselli Franco . 2005 . A new model for learning in graph domains . In Proceedings of IJCNN. 729--734 . Gori Marco, Monfardini Gabriele, and Scarselli Franco. 2005. A new model for learning in graph domains. In Proceedings of IJCNN. 729--734."},{"key":"e_1_3_2_2_22_1","volume-title":"Strong localization in personalized PageRank vectors","author":"Nassar Huda","unstructured":"Huda Nassar , Kyle Kloster , and David F Gleich . 2015. Strong localization in personalized PageRank vectors . In Springer. Springer , 190--202. Huda Nassar, Kyle Kloster, and David F Gleich. 2015. Strong localization in personalized PageRank vectors. In Springer. Springer, 190--202."},{"key":"e_1_3_2_2_23_1","series-title":"SIAM review","volume-title":"The structure and function of complex networks","author":"Newman Mark EJ","year":"2003","unstructured":"Mark EJ Newman . 2003. The structure and function of complex networks . SIAM review ( 2003 ), 167--256. Mark EJ Newman. 2003. The structure and function of complex networks. SIAM review (2003), 167--256."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403168"},{"key":"e_1_3_2_2_25_1","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini.","author":"Scarselli Franco","year":"2009","unstructured":"Franco Scarselli , Marco Gori , Ah Chung Tsoi , Markus Hagenbuchner, and Gabriele Monfardini. 2009 . The Graph Neural Network Model. In IEEE TNN. 61--80. Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009. The Graph Neural Network Model. In IEEE TNN. 61--80."},{"key":"e_1_3_2_2_26_1","volume-title":"Yu","author":"Shi Chuan","year":"2017","unstructured":"Chuan Shi , Yitong Li , Jiawei Zhang , Yizhou Sun , and Philip S . Yu . 2017 . A Survey of Heterogeneous Information Network Analysis. In TKDE. 17--37. Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and Philip S. Yu. 2017. A Survey of Heterogeneous Information Network Analysis. In TKDE. 17--37."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Yizhou Sun Jiawei Han Xifeng Yan Philip S. Yu and Tianyi Wu. 2011. PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks. In VLDB. 992--1003.  Yizhou Sun Jiawei Han Xifeng Yan Philip S. Yu and Tianyi Wu. 2011. PathSim: Meta Path-Based Top-K Similarity Search in Heterogeneous Information Networks. In VLDB. 992--1003.","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_3_2_2_28_1","volume-title":"CoRR","author":"Li Yazhe","unstructured":"A\"a ron van den Oord, Yazhe Li , and Oriol Vinyals . 2018. Representation Learning with Contrastive Predictive Coding . In CoRR , Vol. abs\/ 1807 .03748. A\"a ron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation Learning with Contrastive Predictive Coding. In CoRR, Vol. abs\/1807.03748."},{"key":"e_1_3_2_2_29_1","unstructured":"Andrea Vattani Deepayan Chakrabarti and Maxim Gurevich. 2011. Preserving Personalized Pagerank in Subgraphs.. In ICML. 793--800.  Andrea Vattani Deepayan Chakrabarti and Maxim Gurevich. 2011. Preserving Personalized Pagerank in Subgraphs.. In ICML. 793--800."},{"key":"e_1_3_2_2_30_1","unstructured":"Petar Velickovic William Fedus William L. Hamilton Pietro Li\u00f2 Yoshua Bengio and R. Devon Hjelm. 2019. Deep Graph Infomax. In ICLR.  Petar Velickovic William Fedus William L. Hamilton Pietro Li\u00f2 Yoshua Bengio and R. Devon Hjelm. 2019. Deep Graph Infomax. In ICLR."},{"key":"e_1_3_2_2_31_1","unstructured":"Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In ICML. PMLR 9929--9939.  Tongzhou Wang and Phillip Isola. 2020. Understanding contrastive representation learning through alignment and uniformity on the hypersphere. In ICML. PMLR 9929--9939."},{"key":"e_1_3_2_2_32_1","volume-title":"Yu","author":"Wang Xiao","year":"2019","unstructured":"Xiao Wang , Houye Ji , Chuan Shi , Bai Wang , Yanfang Ye , Peng Cui , and Philip S . Yu . 2019 . Heterogeneous Graph Attention Network. In WWW. 2022--2032. Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S. Yu. 2019. Heterogeneous Graph Attention Network. In WWW. 2022--2032."},{"key":"e_1_3_2_2_33_1","volume-title":"Yu","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu , Shirui Pan , Fengwen Chen , Guodong Long , Chengqi Zhang , and Philip S . Yu . 2020 . A Comprehensive Survey on Graph Neural Networks. In TKDE. Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2020. A Comprehensive Survey on Graph Neural Networks. In TKDE."},{"key":"e_1_3_2_2_34_1","unstructured":"Zhirong Wu Yuanjun Xiong Stella X Yu and Dahua Lin. 2018. Unsupervised feature learning via non-parametric instance discrimination. In CVPR. 3733--3742.  Zhirong Wu Yuanjun Xiong Stella X Yu and Dahua Lin. 2018. Unsupervised feature learning via non-parametric instance discrimination. In CVPR. 3733--3742."},{"key":"e_1_3_2_2_35_1","volume-title":"NIPS","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You , Tianlong Chen , Yongduo Sui , Ting Chen , Zhangyang Wang , and Yang Shen . 2020 . Graph contrastive learning with augmentations . NIPS , Vol. 33 (2020). Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. NIPS, Vol. 33 (2020)."},{"key":"e_1_3_2_2_36_1","volume-title":"et almbox","author":"Zhang Fanjin","year":"2019","unstructured":"Fanjin Zhang , Xiao Liu , Jie Tang , Yuxiao Dong , Peiran Yao , Jie Zhang , Xiaotao Gu , Yan Wang , Bin Shao , Rui Li , et almbox . 2019 . Oag : Toward linking large-scale heterogeneous entity graphs. In SIGKDD. 2585--2595. Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, et almbox. 2019. Oag: Toward linking large-scale heterogeneous entity graphs. In SIGKDD. 2585--2595."}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467396","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467396","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:18:36Z","timestamp":1750191516000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467396"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":36,"alternative-id":["10.1145\/3447548.3467396","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467396","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}