{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,16]],"date-time":"2025-10-16T06:59:50Z","timestamp":1760597990380,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":38,"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":"NSFC-Guangdong Joint Found","award":["U1501254"],"award-info":[{"award-number":["U1501254"]}]},{"name":"Ministry of Science and Technology of the People's Republic of China","award":["2019YBF1600704"],"award-info":[{"award-number":["2019YBF1600704"]}]},{"name":"Guangdong PKL of CPS","award":["2016B030301008"],"award-info":[{"award-number":["2016B030301008"]}]},{"name":"National Natural Science Foundation of China (NSFC)","award":["61603101","6170217361976052"],"award-info":[{"award-number":["61603101","6170217361976052"]}]},{"name":"Macau Science and Technology Development Fund (FDCT)","award":["FDCT\/007\/2016\/AFJ FDCT\/0045\/2019\/A1 SKL-IOTSC-2018-2020"],"award-info":[{"award-number":["FDCT\/007\/2016\/AFJ FDCT\/0045\/2019\/A1 SKL-IOTSC-2018-2020"]}]},{"name":"Guangzhou Science of Technology and Innovation Commission","award":["EF005\/FST-GZG\/2019\/GSTIC"],"award-info":[{"award-number":["EF005\/FST-GZG\/2019\/GSTIC"]}]},{"name":"Universidade de Macau","award":["MYRG2018-00129-FST MYRG2017-00212-FST"],"award-info":[{"award-number":["MYRG2018-00129-FST MYRG2017-00212-FST"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,10,19]]},"DOI":"10.1145\/3340531.3411987","type":"proceedings-article","created":{"date-parts":[[2020,10,19]],"date-time":"2020-10-19T06:18:51Z","timestamp":1603088331000},"page":"545-554","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["GNNVis: Visualize Large-Scale Data by Learning a Graph Neural Network Representation"],"prefix":"10.1145","author":[{"given":"Yajun","family":"Huang","sequence":"first","affiliation":[{"name":"Dingxiang Technologies, Hangzhou, China"}]},{"given":"Jingbin","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Macau, Macau SAR, Macao"}]},{"given":"Yiyang","family":"Yang","sequence":"additional","affiliation":[{"name":"Guangdong University of Technology, Guangzhou, China"}]},{"given":"Zhiguo","family":"Gong","sequence":"additional","affiliation":[{"name":"University of Macau, Macao SAR, Macao"}]},{"given":"Zhifeng","family":"Hao","sequence":"additional","affiliation":[{"name":"Foshan University, Foshan, China"}]}],"member":"320","published-online":{"date-parts":[[2020,10,19]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"585","article-title":"Laplacian eigenmaps and spectral techniques for embedding and clustering","volume":"14","author":"Belkin M.","year":"2001","unstructured":"M. Belkin and P. Niyogi . 2001 . Laplacian eigenmaps and spectral techniques for embedding and clustering . In NIPS , Vol. 14. 585 -- 591 . M. Belkin and P. Niyogi. 2001. Laplacian eigenmaps and spectral techniques for embedding and clustering. In NIPS, Vol. 14. 585--591.","journal-title":"NIPS"},{"key":"e_1_3_2_2_2_1","volume-title":"Canny","author":"Chan David M.","year":"2018","unstructured":"David M. Chan , Roshan Rao , Forrest Huang , and John F . Canny . 2018 . T-SNE-CUDA: GPU- Accelerated T-SNE and its Applications to Modern Data. In SBAC-PAD. 330--338. David M. Chan, Roshan Rao, Forrest Huang, and John F. Canny. 2018. T-SNE-CUDA: GPU-Accelerated T-SNE and its Applications to Modern Data. In SBAC-PAD. 330--338."},{"key":"e_1_3_2_2_3_1","unstructured":"Jie Chen Tengfei Ma and Cao Xiao. 2018a. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR.  Jie Chen Tengfei Ma and Cao Xiao. 2018a. FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling. In ICLR."},{"key":"e_1_3_2_2_4_1","unstructured":"Jianfei Chen Jun Zhu and Le Song. 2018b. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML. 941--949.  Jianfei Chen Jun Zhu and Le Song. 2018b. Stochastic Training of Graph Convolutional Networks with Variance Reduction. In ICML. 941--949."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1198\/jasa.2009.0111"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"crossref","unstructured":"Wei-Lin Chiang Xuanqing Liu Si Si Yang Li Samy Bengio and Cho-Jui Hsieh. 2019. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In KDD. 257--266.  Wei-Lin Chiang Xuanqing Liu Si Si Yang Li Samy Bengio and Cho-Jui Hsieh. 2019. Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks. In KDD. 257--266.","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"crossref","unstructured":"S. Dasgupta and Y. Freund. 2008. Random projection trees and low dimensional manifolds. In STOC. 537--546.  S. Dasgupta and Y. Freund. 2008. Random projection trees and low dimensional manifolds. In STOC. 537--546.","DOI":"10.1145\/1374376.1374452"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"crossref","unstructured":"M. Datar N. Immorlica P. Indyk and V. S. Mirrokni. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In SCG. 253--262.  M. Datar N. Immorlica P. Indyk and V. S. Mirrokni. 2004. Locality-sensitive hashing scheme based on p-stable distributions. In SCG. 253--262.","DOI":"10.1145\/997817.997857"},{"key":"e_1_3_2_2_9_1","first-page":"2579","article-title":"Visualizing data using t-sne","volume":"9","author":"der Maaten L. Van","year":"2008","unstructured":"L. Van der Maaten and G. Hinton . 2008 . Visualizing data using t-sne . JMLR , Vol. 9 , 85 (2008), 2579 -- 2605 . L. Van der Maaten and G. Hinton. 2008. Visualizing data using t-sne. JMLR, Vol. 9, 85 (2008), 2579--2605.","journal-title":"JMLR"},{"key":"e_1_3_2_2_10_1","unstructured":"W. Dong C. Moses and K. Li. 2017. Efficient k-nearest neighbor graph construction for generic similarity measures. In WWW. 577--586.  W. Dong C. Moses and K. Li. 2017. Efficient k-nearest neighbor graph construction for generic similarity measures. In WWW. 577--586."},{"key":"e_1_3_2_2_11_1","unstructured":"Hongyang Gao Zhengyang Wang and Shuiwang Ji. 2018. Large-Scale Learnable Graph Convolutional Networks. In KDD. 1416--1424.  Hongyang Gao Zhengyang Wang and Shuiwang Ji. 2018. Large-Scale Learnable Graph Convolutional Networks. In KDD. 1416--1424."},{"key":"e_1_3_2_2_12_1","first-page":"729","article-title":"A new model for learning in graph domains","volume":"2","author":"Gori M.","year":"2005","unstructured":"M. Gori , G. Monfardini , and F. Scarselli . 2005 . A new model for learning in graph domains . IJCNN , Vol. 2 , 1 (2005), 729 -- 734 . M. Gori, G. Monfardini, and F. Scarselli. 2005. A new model for learning in graph domains. IJCNN, Vol. 2, 1 (2005), 729--734.","journal-title":"IJCNN"},{"key":"e_1_3_2_2_13_1","unstructured":"Michael Gutmann and Aapo Hyv\"a rinen. 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS. 297--304.  Michael Gutmann and Aapo Hyv\"a rinen. 2010. Noise-contrastive estimation: A new estimation principle for unnormalized statistical models. In AISTATS. 297--304."},{"key":"e_1_3_2_2_14_1","first-page":"307","article-title":"Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics","volume":"13","author":"Gutmann Michael","year":"2012","unstructured":"Michael Gutmann and Aapo Hyv\"a rinen. 2012 . Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics . JMLR , Vol. 13 (2012), 307 -- 361 . Michael Gutmann and Aapo Hyv\"a rinen. 2012. Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics. JMLR, Vol. 13 (2012), 307--361.","journal-title":"JMLR"},{"key":"e_1_3_2_2_15_1","unstructured":"W. Hamilton Z. Ying and J. Leskovec. 2018. Inductive representation learning on large graphs. NIPS 1024--1034.  W. Hamilton Z. Ying and J. Leskovec. 2018. Inductive representation learning on large graphs. NIPS 1024--1034."},{"key":"e_1_3_2_2_16_1","volume-title":"Stochastic Neighbor Embedding. In NIPS","volume":"15","author":"Hinton G.E.","unstructured":"G.E. Hinton and S.T. Roweis . 2002 . Stochastic Neighbor Embedding. In NIPS , Vol. 15 . MIT Press, Cambridge, MA, USA, 833--840. G.E. Hinton and S.T. Roweis. 2002. Stochastic Neighbor Embedding. In NIPS, Vol. 15. MIT Press, Cambridge, MA, USA, 833--840."},{"key":"e_1_3_2_2_17_1","unstructured":"A. Szlam J. Bruna W. Zaremba and Y. Lecun. 2014. Spectral networks and locally connected networks on graphs. In ICLR.  A. Szlam J. Bruna W. Zaremba and Y. Lecun. 2014. Spectral networks and locally connected networks on graphs. In ICLR."},{"volume-title":"Principal component analysis","author":"Jolliffe I.","key":"e_1_3_2_2_18_1","unstructured":"I. Jolliffe . 1985. Principal component analysis . Wiley Online Library . I. Jolliffe. 1985. Principal component analysis .Wiley Online Library."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1155\/2000\/19436"},{"key":"e_1_3_2_2_20_1","unstructured":"T. N. Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. ICLR (2017).  T. N. Kipf and M. Welling. 2017. Semi-supervised classification with graph convolutional networks. ICLR (2017)."},{"key":"e_1_3_2_2_21_1","volume-title":"Le and Tomas Mikolov","author":"Quoc","year":"2014","unstructured":"Quoc V. Le and Tomas Mikolov . 2014 . Distributed Representations of Sentences and Documents. In ICML. 1188--1196. Quoc V. Le and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents. In ICML. 1188--1196."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2008.12.017"},{"key":"e_1_3_2_2_23_1","unstructured":"X. Bresson M. Defferrard and P. Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS. 3844--3852.  X. Bresson M. Defferrard and P. Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS. 3844--3852."},{"key":"e_1_3_2_2_24_1","unstructured":"Laurens Maaten. 2009. Learning a parametric embedding by preserving local structure. In AISTATS. 384--391.  Laurens Maaten. 2009. Learning a parametric embedding by preserving local structure. In AISTATS. 384--391."},{"key":"e_1_3_2_2_25_1","volume-title":"Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint","author":"McInnes Leland","year":"2018","unstructured":"Leland McInnes , John Healy , and James Melville . 2018 . Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint (2018). Leland McInnes, John Healy, and James Melville. 2018. Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint (2018)."},{"key":"e_1_3_2_2_26_1","unstructured":"Tomas Mikolov Ilya Sutskever Kai Chen Gregory S. Corrado and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In NIPS. 3111--3119.  Tomas Mikolov Ilya Sutskever Kai Chen Gregory S. Corrado and Jeffrey Dean. 2013. Distributed Representations of Words and Phrases and their Compositionality. In NIPS. 3111--3119."},{"key":"e_1_3_2_2_27_1","unstructured":"Martin R Min Laurens Maaten Zineng Yuan Anthony J Bonner and Zhaolei Zhang. 2010. Deep supervised t-distributed embedding. In ICML. 791--798.  Martin R Min Laurens Maaten Zineng Yuan Anthony J Bonner and Zhaolei Zhang. 2010. Deep supervised t-distributed embedding. In ICML. 791--798."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2323"},{"key":"e_1_3_2_2_29_1","first-page":"61","article-title":"The graph neural network model","volume":"20","author":"Scarselli F.","year":"2009","unstructured":"F. Scarselli , M. Gori , A. C. Tsoi , M. Hagenbuchner , and G. Monfardini . 2009 . The graph neural network model . IEEE TNN , Vol. 20 , 1 (2009), 61 -- 80 . F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini. 2009. The graph neural network model. IEEE TNN, Vol. 20, 1 (2009), 61--80.","journal-title":"IEEE TNN"},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"crossref","unstructured":"Jian Tang Jingzhou Liu Ming Zhang and Qiaozhu Mei. 2016. Visualizing large-scale and high-dimensional data. In WWW. 287--297.  Jian Tang Jingzhou Liu Ming Zhang and Qiaozhu Mei. 2016. Visualizing large-scale and high-dimensional data. In WWW. 287--297.","DOI":"10.1145\/2872427.2883041"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.290.5500.2319"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02288916"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186120"},{"key":"e_1_3_2_2_35_1","first-page":"3221","article-title":"Accelerating t-SNE using tree-based algorithms","volume":"15","author":"Der Maaten Laurens Van","year":"2014","unstructured":"Laurens Van Der Maaten . 2014 . Accelerating t-SNE using tree-based algorithms . JMLR , Vol. 15 , 1 (2014), 3221 -- 3245 . Laurens Van Der Maaten. 2014. Accelerating t-SNE using tree-based algorithms. JMLR, Vol. 15, 1 (2014), 3221--3245.","journal-title":"JMLR"},{"key":"e_1_3_2_2_36_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR.  Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_2_37_1","volume-title":"ESANN","volume":"6","author":"Venna Jarkko","year":"2006","unstructured":"Jarkko Venna and Samuel Kaski . 2006 . Visualizing gene interaction graphs with local multidimensional scaling . In ESANN , Vol. 6 . Citeseer, 557--562. Jarkko Venna and Samuel Kaski. 2006. Visualizing gene interaction graphs with local multidimensional scaling. In ESANN, Vol. 6. Citeseer, 557--562."},{"key":"e_1_3_2_2_38_1","unstructured":"Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.  Han Xiao Kashif Rasul and Roland Vollgraf. 2017. Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms."}],"event":{"name":"CIKM '20: The 29th ACM International Conference on Information and Knowledge Management","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Virtual Event Ireland","acronym":"CIKM '20"},"container-title":["Proceedings of the 29th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411987","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3340531.3411987","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T22:01:23Z","timestamp":1750197683000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3340531.3411987"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,19]]},"references-count":38,"alternative-id":["10.1145\/3340531.3411987","10.1145\/3340531"],"URL":"https:\/\/doi.org\/10.1145\/3340531.3411987","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"}}]}}