{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:41:34Z","timestamp":1776400894650,"version":"3.51.2"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T00:00:00Z","timestamp":1597881600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012659","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61825602"],"award-info":[{"award-number":["61825602"]}],"id":[{"id":"10.13039\/501100012659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,8,23]]},"DOI":"10.1145\/3394486.3403218","type":"proceedings-article","created":{"date-parts":[[2020,8,20]],"date-time":"2020-08-20T23:03:57Z","timestamp":1597964637000},"page":"1666-1676","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":143,"title":["Understanding Negative Sampling in Graph Representation Learning"],"prefix":"10.1145","author":[{"given":"Zhen","family":"Yang","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Ming","family":"Ding","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]},{"given":"Chang","family":"Zhou","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}]},{"given":"Hongxia","family":"Yang","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}]},{"given":"Jingren","family":"Zhou","sequence":"additional","affiliation":[{"name":"DAMO Academy, Alibaba Group, Hangzhou, China"}]},{"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2020,8,20]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2007.912312"},{"key":"e_1_3_2_2_2_1","unstructured":"Avishek Joey Bose Huan Ling and Yanshuai Cao. 2018. Adversarial Contrastive Estimation. (2018) 1021--1032.  Avishek Joey Bose Huan Ling and Yanshuai Cao. 2018. Adversarial Contrastive Estimation. (2018) 1021--1032."},{"key":"e_1_3_2_2_3_1","volume-title":"KBGAN: Adversarial Learning for Knowledge Graph Embeddings. In NAACL-HLT?18. 1470--1480.","author":"Cai Liwei","year":"2018","unstructured":"Liwei Cai and William Yang Wang . 2018 . KBGAN: Adversarial Learning for Knowledge Graph Embeddings. In NAACL-HLT?18. 1470--1480. Liwei Cai and William Yang Wang. 2018. KBGAN: Adversarial Learning for Knowledge Graph Embeddings. In NAACL-HLT?18. 1470--1480."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"crossref","unstructured":"Hugo Caselles-Dupr\u00e9 Florian Lesaint and Jimena Royo-Letelier. 2018. Word2vec applied to recommendation: Hyperparameters matter. In RecSys'18. ACM 352--356.  Hugo Caselles-Dupr\u00e9 Florian Lesaint and Jimena Royo-Letelier. 2018. Word2vec applied to recommendation: Hyperparameters matter. In RecSys'18. ACM 352--356.","DOI":"10.1145\/3240323.3240377"},{"key":"e_1_3_2_2_5_1","volume-title":"ICLR'18","author":"Chen Jie","year":"2018","unstructured":"Jie Chen , Tengfei Ma , and Cao Xiao . 2018 . FastGCN: fast learning with graph convolutional networks via importance sampling . ICLR'18 (2018). Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: fast learning with graph convolutional networks via importance sampling. ICLR'18 (2018)."},{"key":"e_1_3_2_2_6_1","volume-title":"Understanding the metropolis-hastings algorithm. The american statistician","author":"Chib Siddhartha","year":"1995","unstructured":"Siddhartha Chib and Edward Greenberg . 1995. Understanding the metropolis-hastings algorithm. The american statistician , Vol. 49 , 4 ( 1995 ), 327--335. Siddhartha Chib and Edward Greenberg. 1995. Understanding the metropolis-hastings algorithm. The american statistician, Vol. 49, 4 (1995), 327--335."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1864708.1864721"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271768"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/1390681.1442794"},{"key":"e_1_3_2_2_10_1","unstructured":"Hongchang Gao and Heng Huang. 2018. Self-Paced Network Embedding. (2018) 1406--1415.  Hongchang Gao and Heng Huang. 2018. Self-Paced Network Embedding. (2018) 1406--1415."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2188396"},{"key":"e_1_3_2_2_13_1","volume-title":"NIPS'17","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton , Zhitao Ying , and Jure Leskovec . 2017 . Inductive representation learning on large graphs . In NIPS'17 . 1024--1034. Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NIPS'17. 1024--1034."},{"key":"e_1_3_2_2_14_1","volume-title":"Paired t test","author":"Hsu Henry","year":"2007","unstructured":"Henry Hsu and Peter A Lachenbruch . 2007. Paired t test . Wiley encyclopedia of clinical trials ( 2007 ), 1--3. Henry Hsu and Peter A Lachenbruch. 2007. Paired t test. Wiley encyclopedia of clinical trials (2007), 1--3."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.22"},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/2567948.2576940"},{"key":"e_1_3_2_2_17_1","volume-title":"ICLR'17","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . 2017 . Semi-supervised classification with graph convolutional networks . ICLR'17 (2017). Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. ICLR'17 (2017)."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1217299.1217301"},{"key":"e_1_3_2_2_19_1","volume-title":"NIPS'14","author":"Levy Omer","year":"2014","unstructured":"Omer Levy and Yoav Goldberg . 2014 . Neural word embedding as implicit matrix factorization . In NIPS'14 . 2177--2185. Omer Levy and Yoav Goldberg. 2014. Neural word embedding as implicit matrix factorization. In NIPS'14. 2177--2185."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_2_21_1","volume-title":"com recommendations: Item-to-item collaborative filtering","author":"Linden Greg","year":"2003","unstructured":"Greg Linden , Brent Smith , and Jeremy York . 2003. Amazon. com recommendations: Item-to-item collaborative filtering . IEEE Internet computing, Vol. 7 , 1 ( 2003 ), 76--80. Greg Linden, Brent Smith, and Jeremy York. 2003. Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet computing, Vol. 7, 1 (2003), 76--80."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2766462.2767755"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.2172\/4390578"},{"key":"e_1_3_2_2_24_1","volume-title":"NIPS'13","author":"Mikolov Tomas","year":"2013","unstructured":"Tomas Mikolov , Ilya Sutskever , Kai Chen , Greg S Corrado , and Jeff Dean . 2013 . Distributed representations of words and phrases and their compositionality . In NIPS'13 . 3111--3119. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS'13. 3111--3119."},{"key":"e_1_3_2_2_25_1","volume-title":"NIPS'13","author":"Mnih Andriy","year":"2013","unstructured":"Andriy Mnih and Koray Kavukcuoglu . 2013 . Learning word embeddings efficiently with noise-contrastive estimation . In NIPS'13 . 2265--2273. Andriy Mnih and Koray Kavukcuoglu. 2013. Learning word embeddings efficiently with noise-contrastive estimation. In NIPS'13. 2265--2273."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2008.16"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159706"},{"key":"e_1_3_2_2_29_1","volume-title":"BPR: Bayesian personalized ranking from implicit feedback. In UAI'09","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2009 . BPR: Bayesian personalized ranking from implicit feedback. In UAI'09 . AUAI Press , 452--461. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI'09. AUAI Press, 452--461."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1816123.1816129"},{"key":"e_1_3_2_2_31_1","volume-title":"Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197","author":"Sun Zhiqing","year":"2019","unstructured":"Zhiqing Sun , Zhi-Hong Deng , Jian-Yun Nie , and Jian Tang . 2019 . Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019). Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P17-1158"},{"key":"e_1_3_2_2_34_1","volume-title":"ICLR'18","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018 . Graph attention networks . ICLR'18 (2018). Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. ICLR'18 (2018)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3077136.3080786"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220004"},{"key":"e_1_3_2_2_37_1","volume-title":"AAAI'17","author":"Wang Xiao","year":"2017","unstructured":"Xiao Wang , Peng Cui , Jing Wang , Jian Pei , Wenwu Zhu , and Shiqiang Yang . 2017 a. Community preserving network embedding . In AAAI'17 . Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. 2017a. Community preserving network embedding. In AAAI'17."},{"key":"e_1_3_2_2_38_1","volume-title":"IJCAI'11 .","author":"Weston Jason","year":"2011","unstructured":"Jason Weston , Samy Bengio , and Nicolas Usunier . 2011 . Wsabie: Scaling up to large vocabulary image annotation . In IJCAI'11 . Jason Weston, Samy Bengio, and Nicolas Usunier. 2011. Wsabie: Scaling up to large vocabulary image annotation. In IJCAI'11 ."},{"key":"e_1_3_2_2_39_1","volume-title":"How powerful are graph neural networks? arXiv preprint arXiv:1810.00826","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 ( 2018 ). Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How powerful are graph neural networks? arXiv preprint arXiv:1810.00826 (2018)."},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1145\/2484028.2484126"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"crossref","unstructured":"Yongqi Zhang Quanming Yao Yingxia Shao and Lei Chen. 2019. NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding. (2019) 614--625.  Yongqi Zhang Quanming Yao Yingxia Shao and Lei Chen. 2019. NSCaching: Simple and Efficient Negative Sampling for Knowledge Graph Embedding. (2019) 614--625.","DOI":"10.1109\/ICDE.2019.00061"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P18-2090"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806511"},{"key":"e_1_3_2_2_45_1","volume-title":"AAAI'17","author":"Zhou Chang","year":"2017","unstructured":"Chang Zhou , Yuqiong Liu , Xiaofei Liu , Zhongyi Liu , and Jun Gao . 2017 . Scalable graph embedding for asymmetric proximity . In AAAI'17 . Chang Zhou, Yuqiong Liu, Xiaofei Liu, Zhongyi Liu, and Jun Gao. 2017. Scalable graph embedding for asymmetric proximity. In AAAI'17."},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"crossref","unstructured":"Chang Zhou Jianxin Ma Jianwei Zhang Jingren Zhou and Hongxia Yang. 2020. Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems. arxiv: cs.IR\/2005.12964  Chang Zhou Jianxin Ma Jianwei Zhang Jingren Zhou and Hongxia Yang. 2020. Contrastive Learning for Debiased Candidate Generation in Large-Scale Recommender Systems. arxiv: cs.IR\/2005.12964","DOI":"10.1145\/3447548.3467102"}],"event":{"name":"KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event CA USA","acronym":"KDD '20","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 26th ACM SIGKDD International Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403218","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3394486.3403218","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:46Z","timestamp":1750197706000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3394486.3403218"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,20]]},"references-count":46,"alternative-id":["10.1145\/3394486.3403218","10.1145\/3394486"],"URL":"https:\/\/doi.org\/10.1145\/3394486.3403218","relation":{},"subject":[],"published":{"date-parts":[[2020,8,20]]},"assertion":[{"value":"2020-08-20","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}