{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T18:23:57Z","timestamp":1780511037550,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"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":[[2024,8,25]]},"DOI":"10.1145\/3637528.3672047","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T00:54:55Z","timestamp":1724547295000},"page":"2548-2559","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DPHGNN: A Dual Perspective Hypergraph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7402-1785","authenticated-orcid":false,"given":"Siddhant","family":"Saxena","sequence":"first","affiliation":[{"name":"IIT Delhi, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8189-8181","authenticated-orcid":false,"given":"Shounak","family":"Ghatak","sequence":"additional","affiliation":[{"name":"IIT Delhi, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2262-6250","authenticated-orcid":false,"given":"Raghu","family":"Kolla","sequence":"additional","affiliation":[{"name":"Meesho, Bangalore, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1280-2594","authenticated-orcid":false,"given":"Debashis","family":"Mukherjee","sequence":"additional","affiliation":[{"name":"Meesho, Bangalore, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0210-0369","authenticated-orcid":false,"given":"Tanmoy","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"IIT Delhi, New Delhi, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/3183713.3190664"},{"key":"e_1_3_2_1_2_1","volume-title":"HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs. CoRR","author":"Arya Devanshu","year":"2020","unstructured":"Devanshu Arya, Deepak K. Gupta, Stevan Rudinac, and Marcel Worring. 2020. HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs. CoRR, Vol. abs\/2010.04558 (2020). http:\/\/dblp.uni-trier.de\/db\/journals\/corr\/corr2010.html#abs-2010-04558"},{"key":"e_1_3_2_1_3_1","volume-title":"Torr","author":"Bai Song","year":"2019","unstructured":"Song Bai, Feihu Zhang, and Philip H. S. Torr. 2019. Hypergraph Convolution and Hypergraph Attention. CoRR, Vol. abs\/1901.08150 (2019). http:\/\/dblp.uni-trier.de\/db\/journals\/corr\/corr1901.html#abs-1901-08150"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/BigData50022.2020.9378335"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/978--3--319-00080-0"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30786-8_26"},{"key":"e_1_3_2_1_7_1","unstructured":"Guanzi Chen Jiying Zhang Xi Xiao and Yang Li. 2022. Preventing Over-Smoothing for Hypergraph Neural Networks. arxiv: 2203.17159 [cs.LG]"},{"key":"e_1_3_2_1_8_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=hpBTIv2uy_E","author":"Chien Eli","year":"2022","unstructured":"Eli Chien, Chao Pan, Jianhao Peng, and Olgica Milenkovic. 2022. You are AllSet: A Multiset Function Framework for Hypergraph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=hpBTIv2uy_E"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054304"},{"key":"e_1_3_2_1_10_1","volume-title":"HNHN: Hypergraph Networks with Hyperedge Neurons. ArXiv","author":"Dong Yihe","year":"2020","unstructured":"Yihe Dong, Will Sawin, and Yoshua Bengio. 2020. HNHN: Hypergraph Networks with Hyperedge Neurons. ArXiv, Vol. abs\/2006.12278 (2020). https:\/\/api.semanticscholar.org\/CorpusID:219965680"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In The world wide web conference. 417--426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33013558"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3182052"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3039374"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1103\/physreva.101.033816"},{"key":"e_1_3_2_1_16_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In Advances in Neural Information Processing Systems. 11."},{"key":"e_1_3_2_1_17_1","volume-title":"The Second Learning on Graphs Conference. https:\/\/openreview.net\/forum?id=cHuii4NOB9","author":"Hayhoe Mikhail","year":"2023","unstructured":"Mikhail Hayhoe, Hans Matthew Riess, Michael M. Zavlanos, VICTOR PRECIADO, and Alejandro Ribeiro. 2023. Transferable Hypergraph Neural Networks via Spectral Similarity. In The Second Learning on Graphs Conference. https:\/\/openreview.net\/forum?id=cHuii4NOB9"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/353"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP39728.2021.9413523"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403253"},{"key":"e_1_3_2_1_21_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. http:\/\/arxiv.org\/abs\/1609.02907 cite arxiv:1609.02907Comment: Published as a conference paper at ICLR 2017."},{"key":"e_1_3_2_1_22_1","volume-title":"Proceedings of the NetDB","volume":"11","author":"Kreps Jay","year":"2011","unstructured":"Jay Kreps, Neha Narkhede, Jun Rao, et al. 2011. Kafka: A distributed messaging system for log processing. In Proceedings of the NetDB, Vol. 11. Athens, Greece, 1--7."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-21967-2_11"},{"key":"e_1_3_2_1_24_1","unstructured":"Rashmee Lahon. 2022. Return to Origin- Why it Happens Its Impact and How to Solve it? https:\/\/razorpay.com\/blog\/return-to-origin-challenges-and-how-to-solve-it"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_1_26_1","volume-title":"Survey of Graph Neural Networks and Applications. Wireless Communications and Mobile Computing","author":"Liang Fan","year":"2022","unstructured":"Fan Liang, Cheng Qian, Wei Yu, David W. Griffith, and Nada Golmie. 2022. Survey of Graph Neural Networks and Applications. Wireless Communications and Mobile Computing (2022). https:\/\/api.semanticscholar.org\/CorpusID:251192566"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"crossref","unstructured":"Shengyuan Liu Pei Lv Yuzhen Zhang Jie Fu Junjin Cheng Wanqing Li Bing Zhou and Mingliang Xu. 2020. Semi-Dynamic Hypergraph Neural Network for 3D Pose Estimation.. In IJCAI Christian Bessiere (Ed.). ijcai.org 782--788. http:\/\/dblp.uni-trier.de\/db\/conf\/ijcai\/ijcai2020.html#LiuLZFCLZX20 Scheduled for July 2020 Yokohama Japan postponed due to the Corona pandemic..","DOI":"10.24963\/ijcai.2020\/109"},{"key":"e_1_3_2_1_28_1","volume-title":"Provably powerful graph networks. Advances in neural information processing systems","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. 2019. Provably powerful graph networks. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_29_1","volume-title":"Lin (Eds.)","volume":"33","author":"Morris Christopher","year":"2020","unstructured":"Christopher Morris, Gaurav Rattan, and Petra Mutzel. 2020. Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 21824--21840. https:\/\/proceedings.neurips.cc\/paper\/2020\/file\/f81dee42585b3814de199b2e88757f5c-Paper.pdf"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Raffaella Mulas Christian Kuehn Tobias B\u00f6hle and J\u00fcrgen Jost. 2021. Random walks and Laplacians on hypergraphs: When do they match?arxiv: 2106.11663 [math.SP]","DOI":"10.1016\/j.dam.2022.04.009"},{"key":"e_1_3_2_1_31_1","volume-title":"Hypergraph automorphisms. arXiv preprint arXiv:2010.01049","author":"Mulas Raffaella","year":"2020","unstructured":"Raffaella Mulas, Rub\u00e9n J S\u00e1nchez-Garcia, and Ben D MacArthur. 2020. Hypergraph automorphisms. arXiv preprint arXiv:2010.01049 (2020)."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.disc.2021.112372"},{"key":"e_1_3_2_1_33_1","volume-title":"2023 IEEE 39th International Conference on Data Engineering (ICDE)","author":"Saifuddin Khaled Mohammed","year":"2022","unstructured":"Khaled Mohammed Saifuddin, Bri Bumgardnerr, Farhan Tanvir, and Esra Akbas. 2022. HyGNN: Drug-Drug Interaction Prediction via Hypergraph Neural Network. 2023 IEEE 39th International Conference on Data Engineering (ICDE) (2022), 1503--1516. https:\/\/api.semanticscholar.org\/CorpusID:250072419"},{"key":"e_1_3_2_1_34_1","volume-title":"Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein.","author":"Topping Jake","year":"2022","unstructured":"Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein. 2022. Understanding over-squashing and bottlenecks on graphs via curvature. arxiv: 2111.14522 [stat.ML]"},{"key":"e_1_3_2_1_35_1","article-title":"Visualizing data using t-SNE","volume":"9","author":"der Maaten Laurens Van","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research, Vol. 9, 11 (2008).","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_36_1","volume-title":"Graph Attention Networks. ArXiv","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, A. Casanova, A. Romero, P. Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. ArXiv, Vol. abs\/1710.10903 (2018)."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-15719-7_22"},{"key":"e_1_3_2_1_38_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In ICLR. OpenReview.net. http:\/\/dblp.uni-trier.de\/db\/conf\/iclr\/iclr2019.html#XuHLJ19"},{"key":"e_1_3_2_1_39_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"5458","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation Learning on Graphs with Jumping Knowledge Networks.. In ICML (Proceedings of Machine Learning Research, Vol. 80), Jennifer G. Dy and Andreas Krause (Eds.). PMLR, 5449--5458. http:\/\/dblp.uni-trier.de\/db\/conf\/icml\/icml2018.html#XuLTSKJ18"},{"key":"e_1_3_2_1_40_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS) 33. Curran Associates","author":"Yadati Naganand","unstructured":"Naganand Yadati. 2020. Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs. In Advances in Neural Information Processing Systems (NeurIPS) 33. Curran Associates, Inc."},{"key":"e_1_3_2_1_41_1","volume-title":"Hypergcn: A new method for training graph convolutional networks on hypergraphs. Advances in neural information processing systems","author":"Yadati Naganand","year":"2019","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. Advances in neural information processing systems, Vol. 32 (2019)."},{"key":"e_1_3_2_1_42_1","first-page":"1","article-title":"Cross-attention spectral--spatial network for hyperspectral image classification","volume":"60","author":"Yang Kai","year":"2021","unstructured":"Kai Yang, Hao Sun, Chunbo Zou, and Xiaoqiang Lu. 2021. Cross-attention spectral--spatial network for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, Vol. 60 (2021), 1--14.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10955-011-0384-7"},{"key":"e_1_3_2_1_44_1","volume-title":"Graph Neural Networks: A Review of Methods and Applications. ArXiv","author":"Zhou Jie","year":"2018","unstructured":"Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, and Maosong Sun. 2018. Graph Neural Networks: A Review of Methods and Applications. ArXiv, Vol. abs\/1812.08434 (2018). https:\/\/api.semanticscholar.org\/CorpusID:56517517"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/43.784130"}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672047","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3672047","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T17:46:12Z","timestamp":1755798372000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3672047"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":45,"alternative-id":["10.1145\/3637528.3672047","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3672047","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}