{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T15:52:31Z","timestamp":1778860351783,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,8]],"date-time":"2021-03-08T00:00:00Z","timestamp":1615161600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFB0801003"],"award-info":[{"award-number":["2016YFB0801003"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDC02040400"],"award-info":[{"award-number":["XDC02040400"]}]},{"name":"National Social Science Foundation of China","award":["19BSH022"],"award-info":[{"award-number":["19BSH022"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,8]]},"DOI":"10.1145\/3437963.3441783","type":"proceedings-article","created":{"date-parts":[[2021,3,6]],"date-time":"2021-03-06T04:34:28Z","timestamp":1615005268000},"page":"635-643","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":106,"title":["Bipartite Graph Embedding via Mutual Information Maximization"],"prefix":"10.1145","author":[{"given":"Jiangxia","family":"Cao","sequence":"first","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Xixun","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Shu","family":"Guo","sequence":"additional","affiliation":[{"name":"National Computer Network Emergency Response Technical Team\/Coordination Center of China, Beijing, China"}]},{"given":"Luchen","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Tingwen","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences & University of Chinese Academy of Sciences, Beijing, China"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Xiaomi Inc., Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2021,3,8]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Mutual Information Neural Estimation. In International Conference on Machine Learning (ICML) .","author":"Belghazi Mohamed Ishmael","unstructured":"Mohamed Ishmael Belghazi , Aristide Baratin , Sai Rajeswar , Sherjil Ozair , Yoshua Bengio , R. Devon Hjelm , and Aaron C. Courville . 2018 . Mutual Information Neural Estimation. In International Conference on Machine Learning (ICML) . Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, and Aaron C. Courville. 2018. Mutual Information Neural Estimation. In International Conference on Machine Learning (ICML) ."},{"key":"e_1_3_2_1_2_1","volume-title":"A comprehensive survey of graph embedding: Problems, techniques, and applications","author":"Cai Hongyun","year":"2018","unstructured":"Hongyun Cai , Vincent W Zheng , and Kevin Chen-Chuan Chang . 2018. A comprehensive survey of graph embedding: Problems, techniques, and applications . IEEE Transactions on Knowledge and Data Engineering (TKDE) ( 2018 ). Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang. 2018. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Transactions on Knowledge and Data Engineering (TKDE) (2018)."},{"key":"e_1_3_2_1_3_1","volume-title":"A dendrite method for cluster analysis. Communications in Statistics-theory and Methods","author":"Cali'nski Tadeusz","year":"1974","unstructured":"Tadeusz Cali'nski and Jerzy Harabasz . 1974. A dendrite method for cluster analysis. Communications in Statistics-theory and Methods ( 1974 ). Tadeusz Cali'nski and Jerzy Harabasz. 1974. A dendrite method for cluster analysis. Communications in Statistics-theory and Methods (1974)."},{"key":"e_1_3_2_1_4_1","volume-title":"Community detection in sequence similarity networks based on attribute clustering. PloS One","author":"Chowdhary Janamejaya","year":"2017","unstructured":"Janamejaya Chowdhary , Frank E L\u00f6ffler , and Jeremy C Smith . 2017. Community detection in sequence similarity networks based on attribute clustering. PloS One ( 2017 ). Janamejaya Chowdhary, Frank E L\u00f6ffler, and Jeremy C Smith. 2017. Community detection in sequence similarity networks based on attribute clustering. PloS One (2017)."},{"key":"e_1_3_2_1_5_1","volume-title":"A Survey on Network Embedding","author":"Cui Peng","year":"2017","unstructured":"Peng Cui , Xiao Wang , Jian Pei , and Wenwu Zhu . 2017. A Survey on Network Embedding . IEEE Transactions on Knowledge and Data Engineering (TKDE) ( 2017 ). Peng Cui, Xiao Wang, Jian Pei, and Wenwu Zhu. 2017. A Survey on Network Embedding. IEEE Transactions on Knowledge and Data Engineering (TKDE) (2017)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Nitesh V Chawla and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In ACM Knowledge Discovery and Data Mining (KDD) .  Yuxiao Dong Nitesh V Chawla and Ananthram Swami. 2017. metapath2vec: Scalable representation learning for heterogeneous networks. In ACM Knowledge Discovery and Data Mining (KDD) .","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3209978.3209987"},{"key":"e_1_3_2_1_8_1","volume-title":"Learning Vertex Representations for Bipartite Networks. ArXiv","author":"Gao Ming","year":"2019","unstructured":"Ming Gao , Xiangnan He , Leihui Chen , and Aoying Zhou . 2019. Learning Vertex Representations for Bipartite Networks. ArXiv ( 2019 ). Ming Gao, Xiangnan He, Leihui Chen, and Aoying Zhou. 2019. Learning Vertex Representations for Bipartite Networks. ArXiv (2019)."},{"key":"e_1_3_2_1_9_1","volume-title":"International Conference on Machine Learning (ICML) .","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer , Samuel S Schoenholz , Patrick F Riley , Oriol Vinyals , and George E Dahl . 2017 . Neural message passing for quantum chemistry . In International Conference on Machine Learning (ICML) . Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International Conference on Machine Learning (ICML) ."},{"key":"e_1_3_2_1_10_1","volume-title":"Annual Conference on Neural Information Processing Systems (NeurIPS) .","author":"Goodfellow Ian","year":"2014","unstructured":"Ian Goodfellow , Jean Pouget-Abadie , Mehdi Mirza , Bing Xu , David Warde-Farley , Sherjil Ozair , Aaron Courville , and Yoshua Bengio . 2014 . Generative adversarial nets . In Annual Conference on Neural Information Processing Systems (NeurIPS) . Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. In Annual Conference on Neural Information Processing Systems (NeurIPS) ."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In ACM Knowledge Discovery and Data Mining (KDD) .  Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable Feature Learning for Networks. In ACM Knowledge Discovery and Data Mining (KDD) .","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_12_1","volume-title":"Knowledge Graph Embedding Preserving Soft Logical Regularity. In ACM International Conference on Information and Knowledge Management (CIKM) .","author":"Guo Shu","year":"2020","unstructured":"Shu Guo , Lin Li , Zhen Hui , Lingshuai Meng , Bingnan Ma , Wei Liu , Lihong Wang , Haibin Zhai , and Hong Zhang . 2020 . Knowledge Graph Embedding Preserving Soft Logical Regularity. In ACM International Conference on Information and Knowledge Management (CIKM) . Shu Guo, Lin Li, Zhen Hui, Lingshuai Meng, Bingnan Ma, Wei Liu, Lihong Wang, Haibin Zhai, and Hong Zhang. 2020. Knowledge Graph Embedding Preserving Soft Logical Regularity. In ACM International Conference on Information and Knowledge Management (CIKM) ."},{"key":"e_1_3_2_1_13_1","volume-title":"Annual Conference on Neural Information Processing Systems (NeurIPS) .","author":"Hamilton William","year":"2017","unstructured":"William Hamilton , Rex Ying , and Jure Leskovec . 2017 a. Inductive representation learning on large graphs . In Annual Conference on Neural Information Processing Systems (NeurIPS) . William Hamilton, Rex Ying, and Jure Leskovec. 2017a. Inductive representation learning on large graphs. In Annual Conference on Neural Information Processing Systems (NeurIPS) ."},{"key":"e_1_3_2_1_14_1","volume-title":"Representation Learning on Graphs: Methods and Applications","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton , Rex Ying , and Jure Leskovec . 2017b. Representation Learning on Graphs: Methods and Applications . IEEE Data Engineering Bulletin ( 2017 ). William L. Hamilton, Rex Ying, and Jure Leskovec. 2017b. Representation Learning on Graphs: Methods and Applications. IEEE Data Engineering Bulletin (2017)."},{"key":"e_1_3_2_1_15_1","volume-title":"Neural Collaborative Filtering. In International World Wide Web Conferences (WWW) .","author":"He Xiangnan","year":"2017","unstructured":"Xiangnan He , Lizi Liao , Hanwang Zhang , Liqiang Nie , Xia Hu , and Tat-Seng Chua . 2017 . Neural Collaborative Filtering. In International World Wide Web Conferences (WWW) . Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural Collaborative Filtering. In International World Wide Web Conferences (WWW) ."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Learning Representations (ICLR) .","author":"Hjelm R. Devon","year":"2019","unstructured":"R. Devon Hjelm , Alex Fedorov , Samuel Lavoie-Marchildon , Karan Grewal , Adam Trischler , and Yoshua Bengio . 2019 . Learning deep representations by mutual information estimation and maximization . In International Conference on Learning Representations (ICLR) . R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Adam Trischler, and Yoshua Bengio. 2019. Learning deep representations by mutual information estimation and maximization. In International Conference on Learning Representations (ICLR) ."},{"key":"e_1_3_2_1_17_1","volume-title":"Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR) .","author":"Diederik","unstructured":"Diederik P. Kingma and Jimmy Ba. 2014 . Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR) . Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. In International Conference on Learning Representations (ICLR) ."},{"key":"e_1_3_2_1_18_1","volume-title":"International Conference on Learning Representations (ICLR) .","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Max Welling . 2014 . Auto-encoding variational bayes . In International Conference on Learning Representations (ICLR) . Diederik P Kingma and Max Welling. 2014. Auto-encoding variational bayes. In International Conference on Learning Representations (ICLR) ."},{"key":"e_1_3_2_1_19_1","volume-title":"Variational Graph Auto-Encoders. NeurIPS Workshop on Bayesian Deep Learning","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016 . Variational Graph Auto-Encoders. NeurIPS Workshop on Bayesian Deep Learning (2016). Thomas N Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. NeurIPS Workshop on Bayesian Deep Learning (2016)."},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Learning Representations (ICLR) .","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . 2017 . Semi-supervised classification with graph convolutional networks . In International Conference on Learning Representations (ICLR) . Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations (ICLR) ."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00053"},{"key":"e_1_3_2_1_22_1","volume-title":"Exploratory Adversarial Attacks on Graph Neural Networks. In IEEE International Conference On Data Mining (ICDM) .","author":"Lin Xixun","year":"2020","unstructured":"Xixun Lin , Chuan Zhou , Hong Yang , Jia Wu , Haibo Wang , Yanan Cao , and Bin Wang . 2020 . Exploratory Adversarial Attacks on Graph Neural Networks. In IEEE International Conference On Data Mining (ICDM) . Xixun Lin, Chuan Zhou, Hong Yang, Jia Wu, Haibo Wang, Yanan Cao, and Bin Wang. 2020. Exploratory Adversarial Attacks on Graph Neural Networks. In IEEE International Conference On Data Mining (ICDM) ."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2019.2911943"},{"key":"e_1_3_2_1_24_1","volume-title":"Annual Conference on Neural Information Processing Systems (NeurIPS) .","author":"Nowozin Sebastian","year":"2016","unstructured":"Sebastian Nowozin , Botond Cseke , and Ryota Tomioka . 2016 . f-gan: Training generative neural samplers using variational divergence minimization . In Annual Conference on Neural Information Processing Systems (NeurIPS) . Sebastian Nowozin, Botond Cseke, and Ryota Tomioka. 2016. f-gan: Training generative neural samplers using variational divergence minimization. In Annual Conference on Neural Information Processing Systems (NeurIPS) ."},{"key":"e_1_3_2_1_25_1","volume-title":"Estimation of Entropy and Mutual Information. Neural Computation","author":"Paninski Liam","year":"2002","unstructured":"Liam Paninski . 2002. Estimation of Entropy and Mutual Information. Neural Computation ( 2002 ). Liam Paninski. 2002. Estimation of Entropy and Mutual Information. Neural Computation (2002)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380112"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Bryan Perozzi Rami Al-Rfou' and Steven Skiena. 2014. DeepWalk: Online Learning of Social Representations. In ACM Knowledge Discovery and Data Mining (KDD) .  Bryan Perozzi Rami Al-Rfou' and Steven Skiena. 2014. DeepWalk: Online Learning of Social Representations. In ACM Knowledge Discovery and Data Mining (KDD) .","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/371920.372071"},{"key":"e_1_3_2_1_29_1","volume-title":"Annual Conference on Neural Information Processing Systems (NeurIPS) .","author":"Snell Jake","year":"2017","unstructured":"Jake Snell , Kevin Swersky , and Richard Zemel . 2017 . Prototypical networks for few-shot learning . In Annual Conference on Neural Information Processing Systems (NeurIPS) . Jake Snell, Kevin Swersky, and Richard Zemel. 2017. Prototypical networks for few-shot learning. In Annual Conference on Neural Information Processing Systems (NeurIPS) ."},{"key":"e_1_3_2_1_30_1","volume-title":"Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research (JMLR)","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava , Geoffrey Hinton , Alex Krizhevsky , Ilya Sutskever , and Ruslan Salakhutdinov . 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research (JMLR) ( 2014 ). Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. Journal of Machine Learning Research (JMLR) (2014)."},{"key":"e_1_3_2_1_31_1","volume-title":"International Conference on Learning Representations (ICLR) .","author":"Sun Fan-Yun","year":"2020","unstructured":"Fan-Yun Sun , Jordan Hoffmann , and Jian Tang . 2020 . Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization . In International Conference on Learning Representations (ICLR) . Fan-Yun Sun, Jordan Hoffmann, and Jian Tang. 2020. Infograph: Unsupervised and semi-supervised graph-level representation learning via mutual information maximization. In International Conference on Learning Representations (ICLR) ."},{"key":"e_1_3_2_1_32_1","volume-title":"FOBE and HOBE: First- and High-Order Bipartite Embeddings. ArXiv","author":"Sybrandt Justin","year":"2019","unstructured":"Justin Sybrandt and Ilya Safro . 2019. FOBE and HOBE: First- and High-Order Bipartite Embeddings. ArXiv ( 2019 ). Justin Sybrandt and Ilya Safro. 2019. FOBE and HOBE: First- and High-Order Bipartite Embeddings. ArXiv (2019)."},{"key":"e_1_3_2_1_33_1","volume-title":"LINE: Large-scale Information Network Embedding. In International World Wide Web Conferences (WWW) .","author":"Tang Jian","year":"2015","unstructured":"Jian Tang , Meng Qu , Mingzhe Wang , Ming Zhang , Jun Yan , and Qiaozhu Mei . 2015 . LINE: Large-scale Information Network Embedding. In International World Wide Web Conferences (WWW) . Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale Information Network Embedding. In International World Wide Web Conferences (WWW) ."},{"key":"e_1_3_2_1_34_1","volume-title":"Graph convolutional matrix completion. ArXiv","author":"van den Berg Rianne","year":"2017","unstructured":"Rianne van den Berg , Thomas N. Kipf , and Max Welling . 2017. Graph convolutional matrix completion. ArXiv ( 2017 ). Rianne van den Berg, Thomas N. Kipf, and Max Welling. 2017. Graph convolutional matrix completion. ArXiv (2017)."},{"key":"e_1_3_2_1_35_1","volume-title":"Representation Learning with Contrastive Predictive Coding. ArXiv","author":"Li Yazhe","year":"2018","unstructured":"A\"aron van den Oord, Yazhe Li , and Oriol Vinyals . 2018. Representation Learning with Contrastive Predictive Coding. ArXiv ( 2018 ). A\"aron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation Learning with Contrastive Predictive Coding. ArXiv (2018)."},{"key":"e_1_3_2_1_36_1","volume-title":"Deep Graph Infomax. In International Conference on Learning Representations (ICLR) .","author":"Velickovic Petar","unstructured":"Petar Velickovic , William Fedus , William L. Hamilton , Pietro Li\u00f2 , Yoshua Bengio , and R. Devon Hjelm . 2019 . Deep Graph Infomax. In International Conference on Learning Representations (ICLR) . Petar Velickovic, William Fedus, William L. Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R. Devon Hjelm. 2019. Deep Graph Infomax. In International Conference on Learning Representations (ICLR) ."},{"key":"e_1_3_2_1_37_1","volume-title":"Neural Graph Collaborative Filtering. In International Conference on Research on Development in Information Retrieval (SIGIR) .","author":"Wang Xiang","year":"2019","unstructured":"Xiang Wang , Xiangnan He , Meng Wang , Fuli Feng , and Tat-Seng Chua . 2019 . Neural Graph Collaborative Filtering. In International Conference on Research on Development in Information Retrieval (SIGIR) . Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural Graph Collaborative Filtering. In International Conference on Research on Development in Information Retrieval (SIGIR) ."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"crossref","unstructured":"Yoshihiro Yamanishi Masaaki Kotera Minoru Kanehisa and Susumu Goto. 2010. Drug-target interaction prediction from chemical genomic and pharmacological data in an integrated framework. In Bioinformatics .  Yoshihiro Yamanishi Masaaki Kotera Minoru Kanehisa and Susumu Goto. 2010. Drug-target interaction prediction from chemical genomic and pharmacological data in an integrated framework. In Bioinformatics .","DOI":"10.1093\/bioinformatics\/btq176"},{"key":"e_1_3_2_1_40_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 ACM Knowledge Discovery and Data Mining (KDD) .  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 ACM Knowledge Discovery and Data Mining (KDD) .","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_41_1","volume-title":"Unsupervised Attributed Multiplex Network Embedding. In AAAI Conference on Artificial Intelligence (AAAI) .","author":"Park Chan","year":"2020","unstructured":"Chan young Park , Donghyun Kim , Jiawei Han , and Hwanjo Yu . 2020 . Unsupervised Attributed Multiplex Network Embedding. In AAAI Conference on Artificial Intelligence (AAAI) . Chan young Park, Donghyun Kim, Jiawei Han, and Hwanjo Yu. 2020. Unsupervised Attributed Multiplex Network Embedding. In AAAI Conference on Artificial Intelligence (AAAI) ."},{"key":"e_1_3_2_1_42_1","volume-title":"Inductive Matrix Completion Based on Graph Neural Networks. In International Conference on Learning Representations (ICLR) .","author":"Zhang Muhan","year":"2020","unstructured":"Muhan Zhang and Yixin Chen . 2020 . Inductive Matrix Completion Based on Graph Neural Networks. In International Conference on Learning Representations (ICLR) . Muhan Zhang and Yixin Chen. 2020. Inductive Matrix Completion Based on Graph Neural Networks. In International Conference on Learning Representations (ICLR) ."},{"key":"e_1_3_2_1_43_1","volume-title":"Neural IR Meets Graph Embedding: A Ranking Model for Product Search. In International World Wide Web Conferences (WWW) .","author":"Zhang Yuan","year":"2019","unstructured":"Yuan Zhang , Dong Wang , and Yan Zhang . 2019 . Neural IR Meets Graph Embedding: A Ranking Model for Product Search. In International World Wide Web Conferences (WWW) . Yuan Zhang, Dong Wang, and Yan Zhang. 2019. Neural IR Meets Graph Embedding: A Ranking Model for Product Search. In International World Wide Web Conferences (WWW) ."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3132847.3132918"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"crossref","unstructured":"Ziwei Zhang Peng Cui Xiao Wang Jian Pei Xuanrong Yao and Wenwu Zhu. 2018. Arbitrary-order proximity preserved network embedding. In ACM Knowledge Discovery and Data Mining (KDD) .  Ziwei Zhang Peng Cui Xiao Wang Jian Pei Xuanrong Yao and Wenwu Zhu. 2018. Arbitrary-order proximity preserved network embedding. In ACM Knowledge Discovery and Data Mining (KDD) .","DOI":"10.1145\/3219819.3219969"}],"event":{"name":"WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining","location":"Virtual Event Israel","acronym":"WSDM '21","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 14th ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441783","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3437963.3441783","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:35Z","timestamp":1750193255000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3437963.3441783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,8]]},"references-count":45,"alternative-id":["10.1145\/3437963.3441783","10.1145\/3437963"],"URL":"https:\/\/doi.org\/10.1145\/3437963.3441783","relation":{},"subject":[],"published":{"date-parts":[[2021,3,8]]},"assertion":[{"value":"2021-03-08","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}