{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T02:43:41Z","timestamp":1772765021508,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T00:00:00Z","timestamp":1603065600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Ministry of Human Resource Development"},{"name":"SERB Award","award":["ECR\/2017\/003296"],"award-info":[{"award-number":["ECR\/2017\/003296"]}]},{"name":"Pratiksha Trust Young Investigator Award"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,19]]},"DOI":"10.1145\/3340531.3411870","type":"proceedings-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T06:18:51Z","timestamp":1603088331000},"page":"1705-1714","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":75,"title":["NHP: Neural Hypergraph Link Prediction"],"prefix":"10.1145","author":[{"given":"Naganand","family":"Yadati","sequence":"first","affiliation":[{"name":"Indian Institute of Science, Bangalore, India"}]},{"given":"Vikram","family":"Nitin","sequence":"additional","affiliation":[{"name":"Columbia University, New York, NY, USA"}]},{"given":"Madhav","family":"Nimishakavi","sequence":"additional","affiliation":[{"name":"Facebook AI, London, United Kingdom"}]},{"given":"Prateek","family":"Yadav","sequence":"additional","affiliation":[{"name":"LinkedIn, Bangalore, India"}]},{"given":"Anand","family":"Louis","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, Bangalore, India"}]},{"given":"Partha","family":"Talukdar","sequence":"additional","affiliation":[{"name":"Indian Institute of Science, Bangalore, India"}]}],"member":"320","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Devanshu Arya and Marcel Worring. 2018. Exploiting Relational Information in Social Networks Using Geometric Deep Learning on Hypergraphs. In ICMR.  Devanshu Arya and Marcel Worring. 2018. Exploiting Relational Information in Social Networks Using Geometric Deep Learning on Hypergraphs. In ICMR.","DOI":"10.1145\/3206025.3206062"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Haoli Bai Zhuangbin Chen Michael R. Lyu Irwin King and Zenglin Xu. 2018. Neural Relational Topic Models for Scientific Article Analysis. In CIKM.  Haoli Bai Zhuangbin Chen Michael R. Lyu Irwin King and Zenglin Xu. 2018. Neural Relational Topic Models for Scientific Article Analysis. In CIKM.","DOI":"10.1145\/3269206.3271696"},{"key":"e_1_3_2_2_3_1","unstructured":"Peter W. Battaglia Jessica B. Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vin'i cius Flores Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner cC aglar G\u00fc lcc ehre Francis Song Andrew J. Ballard Justin Gilmer George E. Dahl Ashish Vaswani Kelsey Allen Charles Nash Victoria Langston Chris Dyer Nicolas Heess Daan Wierstra Pushmeet Kohli Matthew Botvinick Oriol Vinyals Yujia Li and Razvan Pascanu. 2018. Relational inductive biases deep learning and graph networks. CoRR arXiv:1806.01261 (2018).  Peter W. Battaglia Jessica B. Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vin'i cius Flores Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner cC aglar G\u00fc lcc ehre Francis Song Andrew J. Ballard Justin Gilmer George E. Dahl Ashish Vaswani Kelsey Allen Charles Nash Victoria Langston Chris Dyer Nicolas Heess Daan Wierstra Pushmeet Kohli Matthew Botvinick Oriol Vinyals Yujia Li and Razvan Pascanu. 2018. Relational inductive biases deep learning and graph networks. CoRR arXiv:1806.01261 (2018)."},{"key":"e_1_3_2_2_4_1","volume-title":"HHNE: Heterogeneous Hyper-Network Embedding. In IEEE International Conference on Data Mining (ICDM). 875--880","author":"Baytas Inci M","year":"2018","unstructured":"Inci M Baytas , Cao Xiao , Fei Wang , Anil K. Jain , and Jiayu Zhou . 2018 . HHNE: Heterogeneous Hyper-Network Embedding. In IEEE International Conference on Data Mining (ICDM). 875--880 . Inci M Baytas, Cao Xiao, Fei Wang, Anil K. Jain, and Jiayu Zhou. 2018. HHNE: Heterogeneous Hyper-Network Embedding. In IEEE International Conference on Data Mining (ICDM). 875--880."},{"key":"e_1_3_2_2_5_1","volume-title":"Geometric Deep Learning: Going beyond Euclidean data","author":"Bronstein Michael M.","year":"2017","unstructured":"Michael M. Bronstein , Joan Bruna , Yann LeCun , Arthur Szlam , and Pierre Vandergheynst . 2017. Geometric Deep Learning: Going beyond Euclidean data . IEEE Signal Process. Mag . ( 2017 ). Michael M. Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017. Geometric Deep Learning: Going beyond Euclidean data. IEEE Signal Process. Mag. (2017)."},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.tcs.2019.03.032"},{"key":"e_1_3_2_2_7_1","volume-title":"Zhihao Gavin Tang, and Chenzi Zhang","author":"Hubert Chan T.-H.","year":"2018","unstructured":"T.-H. Hubert Chan , Anand Louis , Zhihao Gavin Tang, and Chenzi Zhang . 2018 . Spectral Properties of Hypergraph Laplacian and Approximation Algorithms. J. ACM ( 2018). T.-H. Hubert Chan, Anand Louis, Zhihao Gavin Tang, and Chenzi Zhang. 2018. Spectral Properties of Hypergraph Laplacian and Approximation Algorithms. J. ACM (2018)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380016"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2020\/303"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"e_1_3_2_2_11_1","volume-title":"Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM). 1679----1688","author":"Gal\u00e1rraga Luis","unstructured":"Luis Gal\u00e1rraga , Geremy Heitz , Kevin Murphy , and Fabian M. Suchanek . 2014. Canonicalizing Open Knowledge Bases . In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM). 1679----1688 . Luis Gal\u00e1rraga, Geremy Heitz, Kevin Murphy, and Fabian M. Suchanek. 2014. Canonicalizing Open Knowledge Bases. In Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management (CIKM). 1679----1688."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"crossref","unstructured":"Giorgio Gallo Giustino Longo Stefano Pallottino and Sang Nguyen. 1993. Directed Hypergraphs and Applications. Discrete Appl. Math. (1993).  Giorgio Gallo Giustino Longo Stefano Pallottino and Sang Nguyen. 1993. Directed Hypergraphs and Applications. Discrete Appl. Math. (1993).","DOI":"10.1016\/0166-218X(93)90045-P"},{"key":"e_1_3_2_2_13_1","volume-title":"Dahl","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 ICML. Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, and George E. Dahl. 2017. Neural Message Passing for Quantum Chemistry. In ICML."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_15_1","unstructured":"William L. Hamilton Rex Ying and Jure Leskovec. 2017a. Inductive Representation Learning on Large Graphs. In NIPS.  William L. Hamilton Rex Ying and Jure Leskovec. 2017a. Inductive Representation Learning on Large Graphs. In NIPS."},{"key":"e_1_3_2_2_16_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 Eng. Bull . ( 2017 ). William L. Hamilton, Rex Ying, and Jure Leskovec. 2017b. Representation Learning on Graphs: Methods and Applications. IEEE Data Eng. Bull. (2017)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3357871"},{"key":"e_1_3_2_2_18_1","unstructured":"Wengong Jin Connor Coley Regina Barzilay and Tommi Jaakkola. 2017. Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. In NIPS.  Wengong Jin Connor Coley Regina Barzilay and Tommi Jaakkola. 2017. Predicting Organic Reaction Outcomes with Weisfeiler-Lehman Network. In NIPS."},{"key":"e_1_3_2_2_19_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In 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_2_20_1","volume-title":"Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications","author":"L\u00fc Linyuan","year":"2011","unstructured":"Linyuan L\u00fc and Tao Zhou . 2011. Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications ( 2011 ). Linyuan L\u00fc and Tao Zhou. 2011. Link prediction in complex networks: A survey. Physica A: Statistical Mechanics and its Applications (2011)."},{"key":"e_1_3_2_2_21_1","unstructured":"Dong Li Zhiming Xu Sheng Li and Xin Sun. 2013. Link Prediction in Social Networks Based on Hypergraph. In WWW.  Dong Li Zhiming Xu Sheng Li and Xin Sun. 2013. Link Prediction in Social Networks Based on Hypergraph. In WWW."},{"key":"e_1_3_2_2_22_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS) 31. Curran Associates","author":"Li Pan","unstructured":"Pan Li , Niao He , and Olgica Milenkovic . 2018. Quadratic Decomposable Submodular Function Minimization . In Advances in Neural Information Processing Systems (NeurIPS) 31. Curran Associates , Inc ., 1054--1064. Pan Li, Niao He, and Olgica Milenkovic. 2018. Quadratic Decomposable Submodular Function Minimization. In Advances in Neural Information Processing Systems (NeurIPS) 31. Curran Associates, Inc., 1054--1064."},{"key":"e_1_3_2_2_23_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS) 30. Curran Associates","author":"Li Pan","unstructured":"Pan Li and Olgica Milenkovic . 2017. Inhomogeneous Hypergraph Clustering with Applications . In Advances in Neural Information Processing Systems (NeurIPS) 30. Curran Associates , Inc ., 2308--2318. Pan Li and Olgica Milenkovic. 2017. Inhomogeneous Hypergraph Clustering with Applications. In Advances in Neural Information Processing Systems (NeurIPS) 30. Curran Associates, Inc., 2308--2318."},{"key":"e_1_3_2_2_24_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (ICML). 3014--3023","author":"Li Pan","year":"2018","unstructured":"Pan Li and Olgica Milenkovic . 2018 . Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering . In Proceedings of the 35th International Conference on Machine Learning (ICML). 3014--3023 . Pan Li and Olgica Milenkovic. 2018. Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering. In Proceedings of the 35th International Conference on Machine Learning (ICML). 3014--3023."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"crossref","unstructured":"David Liben-Nowell and Jon Kleinberg. 2003. The Link Prediction Problem for Social Networks. In CIKM.  David Liben-Nowell and Jon Kleinberg. 2003. The Link Prediction Problem for Social Networks. In CIKM.","DOI":"10.1145\/956863.956972"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611974973.6"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"crossref","unstructured":"Anand Louis. 2015. Hypergraph Markov Operators Eigenvalues Approximation Algorithms. In STOC.  Anand Louis. 2015. Hypergraph Markov Operators Eigenvalues Approximation Algorithms. In STOC.","DOI":"10.1145\/2746539.2746555"},{"key":"e_1_3_2_2_28_1","unstructured":"Federico Monti Michael Bronstein and Xavier Bresson. 2017. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. In NIPS.  Federico Monti Michael Bronstein and Xavier Bresson. 2017. Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks. In NIPS."},{"key":"e_1_3_2_2_29_1","volume-title":"A Review of Relational Machine Learning for Knowledge Graphs. Proc","author":"Nickel Maximilian","year":"2016","unstructured":"Maximilian Nickel , Kevin Murphy , Volker Tresp , and Evgeniy Gabrilovich . 2016. A Review of Relational Machine Learning for Knowledge Graphs. Proc . IEEE ( 2016 ). Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. 2016. A Review of Relational Machine Learning for Knowledge Graphs. Proc. IEEE (2016)."},{"key":"e_1_3_2_2_30_1","volume-title":"Maranas","author":"Kumar Vinay Satish","year":"2007","unstructured":"Vinay Satish Kumar , Madhukar S. Dasika , and Costas D . Maranas . 2007 . Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics ( 2007). Vinay Satish Kumar, Madhukar S. Dasika, and Costas D. Maranas. 2007. Optimization based automated curation of metabolic reconstructions. BMC Bioinformatics (2007)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11266"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186030"},{"key":"e_1_3_2_2_33_1","volume-title":"Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. In International Conference on Artificial Intelligence and Statistics (AISTATS).","author":"Vashishth Shikhar","year":"2019","unstructured":"Shikhar Vashishth , Prateek Yadav , Manik Bhandari , and Partha Talukdar . 2019 . Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. In International Conference on Artificial Intelligence and Statistics (AISTATS). Shikhar Vashishth, Prateek Yadav, Manik Bhandari, and Partha Talukdar. 2019. Confidence-based Graph Convolutional Networks for Semi-Supervised Learning. In International Conference on Artificial Intelligence and Statistics (AISTATS)."},{"key":"e_1_3_2_2_34_1","unstructured":"Petar Velivc kovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR.  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_35_1","unstructured":"Petar Velivc kovi\u0107 William Fedus William L. Hamilton Pietro Li\u00f3 Yoshua Bengio and R Devon Hjelm. 2019. Deep Graph Infomax. In International Confernece on Learning Representations (ICLR).  Petar Velivc kovi\u0107 William Fedus William L. Hamilton Pietro Li\u00f3 Yoshua Bengio and R Devon Hjelm. 2019. Deep Graph Infomax. In International Confernece on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.2307\/2332510"},{"key":"e_1_3_2_2_37_1","volume-title":"Yu","author":"Wu Zonghan","year":"2019","unstructured":"Zonghan Wu , Shirui Pan , Fengwen Chen , Guodong Long , Chengqi Zhang , and Philip S . Yu . 2019 . A Comprehensive Survey on Graph Neural Networks. CoRR , arXiv:1901.00596 (2019). Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and Philip S. Yu. 2019. A Comprehensive Survey on Graph Neural Networks. CoRR, arXiv:1901.00596 (2019)."},{"key":"e_1_3_2_2_38_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS) 32. Curran Associates","author":"Yadati Naganand","unstructured":"Naganand Yadati , Madhav Nimishakavi , Prateek Yadav , Vikram Nitin , Anand Louis , and Partha Talukdar . 2019. HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs . In Advances in Neural Information Processing Systems (NeurIPS) 32. Curran Associates , Inc ., 1509--1520. Naganand Yadati, Madhav Nimishakavi, Prateek Yadav, Vikram Nitin, Anand Louis, and Partha Talukdar. 2019. HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs. In Advances in Neural Information Processing Systems (NeurIPS) 32. Curran Associates, Inc., 1509--1520."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3269274"},{"key":"e_1_3_2_2_40_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning (ICML). 4026--4034","author":"Zhang Chenzi","year":"2017","unstructured":"Chenzi Zhang , Shuguang Hu , Zhihao Gavin Tang , and T-H. Hubert Chan . 2017 . Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method . In Proceedings of the 34th International Conference on Machine Learning (ICML). 4026--4034 . Chenzi Zhang, Shuguang Hu, Zhihao Gavin Tang, and T-H. Hubert Chan. 2017. Re-revisiting Learning on Hypergraphs: Confidence Interval and Subgradient Method. In Proceedings of the 34th International Conference on Machine Learning (ICML). 4026--4034."},{"key":"e_1_3_2_2_41_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS) 31. Curran Associates","author":"Zhang Muhan","unstructured":"Muhan Zhang and Yixin Chen . 2018. Link Prediction Based on Graph Neural Networks . In Advances in Neural Information Processing Systems (NeurIPS) 31. Curran Associates , Inc ., 5171--5181. Muhan Zhang and Yixin Chen. 2018. Link Prediction Based on Graph Neural Networks. In Advances in Neural Information Processing Systems (NeurIPS) 31. Curran Associates, Inc., 5171--5181."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11780"},{"key":"e_1_3_2_2_43_1","unstructured":"Ruochi Zhang Yuesong Zou and Jian Ma. 2020. Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. In International Confernece on Learning Representations (ICLR).  Ruochi Zhang Yuesong Zou and Jian Ma. 2020. Hyper-SAGNN: a self-attention based graph neural network for hypergraphs. In International Confernece on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_44_1","volume-title":"Proceedings of the 19th International Conference on Neural Information Processing Systems (NeurIPS).","author":"Zhou Dengyong","year":"2006","unstructured":"Dengyong Zhou , Jiayuan Huang , and Bernhard Sch\u00f6 lkopf. 2006 . Learning with Hypergraphs: Clustering, Classification, and Embedding . In Proceedings of the 19th International Conference on Neural Information Processing Systems (NeurIPS). Dengyong Zhou, Jiayuan Huang, and Bernhard Sch\u00f6 lkopf. 2006. Learning with Hypergraphs: Clustering, Classification, and Embedding. In Proceedings of the 19th International Conference on Neural Information Processing Systems (NeurIPS)."}],"event":{"name":"CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Ireland","acronym":"CIKM '20","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 29th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411870","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340531.3411870","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:25:49Z","timestamp":1750206349000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411870"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,19]]},"references-count":44,"alternative-id":["10.1145\/3340531.3411870","10.1145\/3340531"],"URL":"https:\/\/doi.org\/10.1145\/3340531.3411870","relation":{},"subject":[],"published":{"date-parts":[[2020,10,19]]},"assertion":[{"value":"2020-10-19","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}