{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,11]],"date-time":"2025-12-11T07:40:08Z","timestamp":1765438808410,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,8,14]],"date-time":"2022-08-14T00:00:00Z","timestamp":1660435200000},"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":[[2022,8,14]]},"DOI":"10.1145\/3534678.3539068","type":"proceedings-article","created":{"date-parts":[[2022,8,12]],"date-time":"2022-08-12T19:06:41Z","timestamp":1660331201000},"page":"4391-4401","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Embedding Compression with Hashing for Efficient Representation Learning in Large-Scale Graph"],"prefix":"10.1145","author":[{"given":"Chin-Chia Michael","family":"Yeh","sequence":"first","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}]},{"given":"Mengting","family":"Gu","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}]},{"given":"Yan","family":"Zheng","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}]},{"given":"Huiyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}]},{"given":"Javid","family":"Ebrahimi","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}]},{"given":"Zhongfang","family":"Zhuang","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}]},{"given":"Junpeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Visa Research, Palo Alto, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Robert Endre Tarjan, et al","author":"Blum Manuel","year":"1973","unstructured":"Manuel Blum, Robert W. Floyd, Vaughan R. Pratt, Ronald L. Rivest, Robert Endre Tarjan, et al. 1973. Time bounds for selection. J. Comput. Syst. Sci. (1973)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_2_1","DOI":"10.1016\/0022-0000(79)90044-8"},{"doi-asserted-by":"crossref","unstructured":"Moses S Charikar. 2002. Similarity estimation techniques from rounding algorithms. In STOC. 380--388.","key":"e_1_3_2_2_3_1","DOI":"10.1145\/509907.509965"},{"unstructured":"Ting Chen Lala Li and Yizhou Sun. 2020. Differentiable product quantization for end-to-end embedding compression. In ICML.","key":"e_1_3_2_2_4_1"},{"unstructured":"Alexis Conneau Guillaume Lample Marc'Aurelio Ranzato Ludovic Denoyer and Herv\u00e9 J\u00e9gou. 2017. Link for word similarity tasks. https:\/\/dl.fbaipublicfiles.com\/arrival\/wordsim.tar.gz.","key":"e_1_3_2_2_5_1"},{"doi-asserted-by":"crossref","unstructured":"Wei Deng Junwei Pan Tian Zhou Deguang Kong Aaron Flores and Guang Lin. 2021. DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving. In WSDM. 922--930.","key":"e_1_3_2_2_6_1","DOI":"10.1145\/3437963.3441727"},{"doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Nitesh V Chawla and Ananthram Swami. 2017a. Link for metapath2vec. https:\/\/ericdongyx.github.io\/metapath2vec\/m2v.html.","key":"e_1_3_2_2_7_1","DOI":"10.1145\/3097983.3098036"},{"doi-asserted-by":"crossref","unstructured":"Yuxiao Dong Nitesh V Chawla and Ananthram Swami. 2017b. metapath2vec: Scalable representation learning for heterogeneous networks. In SIGKDD.","key":"e_1_3_2_2_8_1","DOI":"10.1145\/3097983.3098036"},{"doi-asserted-by":"crossref","unstructured":"Min Du Robert Christensen Wei Zhang and Feifei Li. 2019. Pcard: personalized restaurants recommendation from card payment transaction records. In WWW.","key":"e_1_3_2_2_9_1","DOI":"10.1145\/3308558.3313494"},{"key":"e_1_3_2_2_10_1","volume-title":"Ha The Hien Dang, Quoc Viet Hung Nguyen, and Karl Aberer.","author":"Duong Chi Thang","year":"2019","unstructured":"Chi Thang Duong, Thanh Dat Hoang, Ha The Hien Dang, Quoc Viet Hung Nguyen, and Karl Aberer. 2019. On node features for graph neural networks. arXiv preprint arXiv:1911.08795 (2019)."},{"doi-asserted-by":"crossref","unstructured":"Manaal Faruqui and Chris Dyer. 2014. Community evaluation and exchange of word vectors at wordvectors.org. In ACL: System Demonstrations. 19--24.","key":"e_1_3_2_2_11_1","DOI":"10.3115\/v1\/P14-5004"},{"key":"e_1_3_2_2_12_1","volume-title":"Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428","author":"Fey Matthias","year":"2019","unstructured":"Matthias Fey and Jan Eric Lenssen. 2019. Fast graph representation learning with PyTorch Geometric. arXiv preprint arXiv:1903.02428 (2019)."},{"key":"e_1_3_2_2_13_1","volume-title":"NeurIPS","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. NeurIPS, Vol. 30 (2017)."},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_14_1","DOI":"10.1145\/3397271.3401063"},{"unstructured":"Xiangnan He Lizi Liao Hanwang Zhang Liqiang Nie Xia Hu and Tat-Seng Chua. 2017. Neural collaborative filtering. In WWW.","key":"e_1_3_2_2_15_1"},{"key":"e_1_3_2_2_16_1","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume":"33","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. NeurIPS, Vol. 33 (2020), 22118--22133.","journal-title":"NeurIPS"},{"unstructured":"Ben Johnson William L Hamilton and Can G\u00fcney Aksakalli. 2018. pytorch-graphsage. https:\/\/github.com\/bkj\/pytorch-graphsage.","key":"e_1_3_2_2_17_1"},{"unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.","key":"e_1_3_2_2_18_1"},{"unstructured":"Guillaume Lample Alexis Conneau Ludovic Denoyer and Marc'Aurelio Ranzato. 2018a. Unsupervised Machine Translation Using Monolingual Corpora Only. In ICLR.","key":"e_1_3_2_2_19_1"},{"unstructured":"Guillaume Lample Alexis Conneau Marc'Aurelio Ranzato Ludovic Denoyer and Herv\u00e9 J\u00e9gou. 2018b. Word translation without parallel data. In ICLR.","key":"e_1_3_2_2_20_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_21_1","DOI":"10.1109\/TIT.1982.1056489"},{"unstructured":"Ilya Loshchilov and Frank Hutter. 2018. Decoupled Weight Decay Regularization. In ICLR.","key":"e_1_3_2_2_22_1"},{"unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013a. Distributed representations of words and phrases and their compositionality. In NeurIPS.","key":"e_1_3_2_2_23_1"},{"unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013b. Link for word2vec. https:\/\/code.google.com\/archive\/p\/word2vec\/.","key":"e_1_3_2_2_24_1"},{"key":"e_1_3_2_2_25_1","first-page":"8026","article-title":"Pytorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. Pytorch: An imperative style, high-performance deep learning library. NeurIPS, Vol. 32 (2019), 8026--8037.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_26_1","volume-title":"Glove: Global vectors for word representation. In EMNLP. 1532--1543.","author":"Pennington Jeffrey","year":"2014","unstructured":"Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014a. Glove: Global vectors for word representation. In EMNLP. 1532--1543."},{"unstructured":"Jeffrey Pennington Richard Socher and Christopher D Manning. 2014b. Link for GloVe.6B. https:\/\/nlp.stanford.edu\/data\/glove.6B.zip.","key":"e_1_3_2_2_27_1"},{"unstructured":"Zongyue Qin Yunsheng Bai and Yizhou Sun. 2020. GHashing: Semantic Graph Hashing for Approximate Similarity Search in Graph Databases. In SIGKDD.","key":"e_1_3_2_2_28_1"},{"unstructured":"Raphael Shu and Hideki Nakayama. 2018. Compressing Word Embeddings via Deep Compositional Code Learning. In ICLR.","key":"e_1_3_2_2_29_1"},{"doi-asserted-by":"crossref","unstructured":"Jun Suzuki and Masaaki Nagata. 2016. Learning compact neural word embeddings by parameter space sharing. In IJCAI. 2046--2052.","key":"e_1_3_2_2_30_1","DOI":"10.18653\/v1\/N16-1135"},{"key":"e_1_3_2_2_31_1","volume-title":"Jonas Meinertz Hansen, and Ole Winther","author":"Svenstrup Dan","year":"2017","unstructured":"Dan Svenstrup, Jonas Meinertz Hansen, and Ole Winther. 2017. Hash embeddings for efficient word representations. arXiv preprint arXiv:1709.03933 (2017)."},{"key":"e_1_3_2_2_32_1","first-page":"3775","article-title":"All word embeddings from one embedding","volume":"33","author":"Takase Sho","year":"2020","unstructured":"Sho Takase and Sosuke Kobayashi. 2020. All word embeddings from one embedding. NeurIPS, Vol. 33 (2020), 3775--3785.","journal-title":"NeurIPS"},{"doi-asserted-by":"crossref","unstructured":"Sho Takase and Naoaki Okazaki. 2019. Positional Encoding to Control Output Sequence Length. In NAACL-HLT. 3999--4004.","key":"e_1_3_2_2_33_1","DOI":"10.18653\/v1\/N19-1401"},{"unstructured":"Qiaoyu Tan Ninghao Liu Xing Zhao Hongxia Yang Jingren Zhou and Xia Hu. 2020. Learning to hash with graph neural networks for recommender systems. In WWW.","key":"e_1_3_2_2_34_1"},{"doi-asserted-by":"crossref","unstructured":"Jie Tang Jing Zhang Limin Yao Juanzi Li Li Zhang and Zhong Su. 2008. Arnetminer: extraction and mining of academic social networks. In SIGKDD.","key":"e_1_3_2_2_35_1","DOI":"10.1145\/1401890.1402008"},{"unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez Lukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In NeurIPS.","key":"e_1_3_2_2_36_1"},{"doi-asserted-by":"crossref","unstructured":"Xiang Wang Xiangnan He Meng Wang Fuli Feng and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174.","key":"e_1_3_2_2_37_1","DOI":"10.1145\/3331184.3331267"},{"unstructured":"Felix Wu Amauri Souza Tianyi Zhang Christopher Fifty Tao Yu and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In ICML.","key":"e_1_3_2_2_38_1"},{"unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2018. How Powerful are Graph Neural Networks?. In ICLR.","key":"e_1_3_2_2_39_1"},{"volume-title":"2020 a. Towards a flexible embedding learning framework","author":"Michael Yeh Chin-Chia","unstructured":"Chin-Chia Michael Yeh, Dhruv Gelda, Zhongfang Zhuang, Yan Zheng, Liang Gou, and Wei Zhang. 2020 a. Towards a flexible embedding learning framework. In ICDMW. IEEE, 605--612.","key":"e_1_3_2_2_40_1"},{"unstructured":"Chin-Chia Michael Yeh Mengting Gu Yan Zheng Huiyuan Chen Javid Ebrahimi Zhongfang Zhuang Junpeng Wang Liang Wang and Wei Zhang. 2022. Source Code. https:\/\/www.dropbox.com\/s\/1mixmhgbg4wiwtd\/release.zip .","key":"e_1_3_2_2_41_1"},{"volume-title":"Big Data","author":"Michael Yeh Chin-Chia","unstructured":"Chin-Chia Michael Yeh, Zhongfang Zhuang, Yan Zheng, Liang Wang, Junpeng Wang, and Wei Zhang. 2020 b. Merchant Category Identification Using Credit Card Transactions. In Big Data. IEEE, 1736--1744.","key":"e_1_3_2_2_42_1"},{"doi-asserted-by":"publisher","key":"e_1_3_2_2_43_1","DOI":"10.1016\/j.aiopen.2021.01.001"}],"event":{"sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"acronym":"KDD '22","name":"KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Washington DC USA"},"container-title":["Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539068","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3534678.3539068","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:09:50Z","timestamp":1750183790000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3534678.3539068"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,14]]},"references-count":43,"alternative-id":["10.1145\/3534678.3539068","10.1145\/3534678"],"URL":"https:\/\/doi.org\/10.1145\/3534678.3539068","relation":{},"subject":[],"published":{"date-parts":[[2022,8,14]]},"assertion":[{"value":"2022-08-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}