{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T16:13:32Z","timestamp":1774023212078,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":48,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,7,25]],"date-time":"2019-07-25T00:00:00Z","timestamp":1564012800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1836945, 1836938, 1836866, 1845666, 1852606, 1838627, 1837956"],"award-info":[{"award-number":["1836945, 1836938, 1836866, 1845666, 1852606, 1838627, 1837956"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,7,25]]},"DOI":"10.1145\/3292500.3330888","type":"proceedings-article","created":{"date-parts":[[2019,7,26]],"date-time":"2019-07-26T13:17:26Z","timestamp":1564147046000},"page":"276-284","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":33,"title":["Conditional Random Field Enhanced Graph Convolutional Neural Networks"],"prefix":"10.1145","author":[{"given":"Hongchang","family":"Gao","sequence":"first","affiliation":[{"name":"University of Pittsburgh & JD Finance America Corporation, Pittsburgh, PA, USA"}]},{"given":"Jian","family":"Pei","sequence":"additional","affiliation":[{"name":"Simon Fraser University, Burnaby, Canada"}]},{"given":"Heng","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Pittsburgh & JD Finance America Corporation, Pittsburgh, PA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,7,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Rie K Ando and Tong Zhang. 2007. Learning on graph with Laplacian regularization. In Advances in neural information processing systems. 25--32. Rie K Ando and Tong Zhang. 2007. Learning on graph with Laplacian regularization. In Advances in neural information processing systems. 25--32.","DOI":"10.7551\/mitpress\/7503.003.0009"},{"key":"e_1_3_2_1_2_1","unstructured":"James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in Neural Information Processing Systems. 1993--2001. James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in Neural Information Processing Systems. 1993--2001."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1248547.1248632"},{"key":"e_1_3_2_1_4_1","volume-title":"Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096","author":"Brock Andrew","year":"2018"},{"key":"e_1_3_2_1_5_1","volume-title":"Spectral networks and locally connected networks on graphs. arXiv preprint arXiv:1312.6203","author":"Bruna Joan","year":"2013"},{"key":"e_1_3_2_1_6_1","unstructured":"Miguel Carreira-Perpinan and Weiran Wang. 2014. Distributed optimization of deeply nested systems. In Artificial Intelligence and Statistics . 10--19. Miguel Carreira-Perpinan and Weiran Wang. 2014. Distributed optimization of deeply nested systems. In Artificial Intelligence and Statistics . 10--19."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"Dapeng Chen Dan Xu Hongsheng Li Nicu Sebe and Xiaogang Wang. 2018c. Group Consistent Similarity Learning via Deep CRF for Person Re-Identification. In CVPR . Dapeng Chen Dan Xu Hongsheng Li Nicu Sebe and Xiaogang Wang. 2018c. Group Consistent Similarity Learning via Deep CRF for Person Re-Identification. In CVPR .","DOI":"10.1109\/CVPR.2018.00902"},{"key":"e_1_3_2_1_8_1","volume-title":"FastGCN: fast learning with graph convolutional networks via importance sampling. arXiv preprint arXiv:1801.10247","author":"Chen Jie","year":"2018"},{"key":"e_1_3_2_1_9_1","volume-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","author":"Chen Liang-Chieh","year":"2018"},{"key":"e_1_3_2_1_10_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3844--3852. Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems. 3844--3852."},{"key":"e_1_3_2_1_11_1","unstructured":"David K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Al\u00e1n Aspuru-Guzik and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In Advances in neural information processing systems. 2224--2232. David K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Al\u00e1n Aspuru-Guzik and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In Advances in neural information processing systems. 2224--2232."},{"key":"e_1_3_2_1_12_1","unstructured":"Hongchang Gao and Heng Huang. 2018a. Deep Attributed Network Embedding.. In IJCAI . Hongchang Gao and Heng Huang. 2018a. Deep Attributed Network Embedding.. In IJCAI ."},{"key":"e_1_3_2_1_13_1","unstructured":"Hongchang Gao and Heng Huang. 2018b. Joint Generative Moment-Matching Network for Learning Structural Latent Code.. In IJCAI . Hongchang Gao and Heng Huang. 2018b. Joint Generative Moment-Matching Network for Learning Structural Latent Code.. In IJCAI ."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220041"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330866"},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics. 249--256","author":"Glorot Xavier","year":"2010"},{"key":"e_1_3_2_1_17_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034. Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024--1034."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_1_19_1","unstructured":"Xiaofei He and Partha Niyogi. 2004. Locality preserving projections. In Advances in neural information processing systems. 153--160. Xiaofei He and Partha Niyogi. 2004. Locality preserving projections. In Advances in neural information processing systems. 153--160."},{"key":"e_1_3_2_1_20_1","volume-title":"Deep convolutional networks on graph-structured data. arXiv preprint arXiv:1506.05163","author":"Henaff Mikael","year":"2015"},{"key":"e_1_3_2_1_21_1","volume-title":"Vain: Attentional multi-agent predictive modeling. In Advances in Neural Information Processing Systems. 2701--2711.","author":"Hoshen Yedid","year":"2017"},{"key":"e_1_3_2_1_22_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014"},{"key":"e_1_3_2_1_23_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016"},{"key":"e_1_3_2_1_24_1","volume-title":"Combining Neural Networks with Personalized PageRank for Classification on Graphs. arXiv","author":"Klicpera Johannes","year":"2018"},{"key":"e_1_3_2_1_25_1","unstructured":"Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105. Alex Krizhevsky Ilya Sutskever and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105."},{"key":"e_1_3_2_1_26_1","unstructured":"John Lafferty Andrew McCallum and Fernando CN Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. (2001). John Lafferty Andrew McCallum and Fernando CN Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. (2001)."},{"key":"e_1_3_2_1_27_1","unstructured":"Zhuwen Li Qifeng Chen and Vladlen Koltun. 2018. Combinatorial optimization with graph convolutional networks and guided tree search. In Advances in Neural Information Processing Systems. 537--546. Zhuwen Li Qifeng Chen and Vladlen Koltun. 2018. Combinatorial optimization with graph convolutional networks and guided tree search. In Advances in Neural Information Processing Systems. 537--546."},{"key":"e_1_3_2_1_28_1","unstructured":"Xiaodan Liang Zhiting Hu Hao Zhang Liang Lin and Eric P Xing. 2018. Symbolic graph reasoning meets convolutions. In Advances in Neural Information Processing Systems. 1858--1868. Xiaodan Liang Zhiting Hu Hao Zhang Liang Lin and Eric P Xing. 2018. Symbolic graph reasoning meets convolutions. In Advances in Neural Information Processing Systems. 1858--1868."},{"key":"e_1_3_2_1_29_1","volume-title":"Proceedings of the 20th International Conference on Machine Learning (ICML-03)","author":"Lu Qing","year":"2003"},{"key":"e_1_3_2_1_30_1","volume-title":"CGNF: Conditional Graph Neural Fields.","author":"Ma Tengfei","year":"2018"},{"key":"e_1_3_2_1_31_1","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"van der Maaten Laurens","year":"2008","journal-title":"Journal of machine learning research"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.576"},{"key":"e_1_3_2_1_33_1","unstructured":"Medhini Narasimhan Svetlana Lazebnik and Alexander Schwing. 2018. Out of the box: Reasoning with graph convolution nets for factual visual question answering. In Advances in Neural Information Processing Systems. 2659--2670. Medhini Narasimhan Svetlana Lazebnik and Alexander Schwing. 2018. Out of the box: Reasoning with graph convolution nets for factual visual question answering. In Advances in Neural Information Processing Systems. 2659--2670."},{"key":"e_1_3_2_1_34_1","volume-title":"International conference on machine learning . 2014--2023","author":"Niepert Mathias","year":"2016"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_36_1","volume-title":"Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434","author":"Radford Alec","year":"2015"},{"key":"e_1_3_2_1_37_1","volume-title":"Nonlinear dimensionality reduction by locally linear embedding. science","author":"Roweis Sam T","year":"2000"},{"key":"e_1_3_2_1_38_1","unstructured":"Adam Santoro David Raposo David G Barrett Mateusz Malinowski Razvan Pascanu Peter Battaglia and Timothy Lillicrap. 2017. A simple neural network module for relational reasoning. In Advances in neural information processing systems. 4967--4976. Adam Santoro David Raposo David G Barrett Mateusz Malinowski Razvan Pascanu Peter Battaglia and Timothy Lillicrap. 2017. A simple neural network module for relational reasoning. In Advances in neural information processing systems. 4967--4976."},{"key":"e_1_3_2_1_39_1","volume-title":"Collective classification in network data. AI magazine","author":"Sen Prithviraj","year":"2008"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.5555\/2627435.2670313"},{"key":"e_1_3_2_1_41_1","unstructured":"Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998--6008. Ashish Vaswani Noam Shazeer Niki Parmar Jakob Uszkoreit Llion Jones Aidan N Gomez \u0141ukasz Kaiser and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems. 5998--6008."},{"key":"e_1_3_2_1_42_1","volume-title":"Neural Networks: Tricks of the Trade","author":"Weston Jason"},{"key":"e_1_3_2_1_43_1","volume-title":"Revisiting semi-supervised learning with graph embeddings. arXiv preprint arXiv:1603.08861","author":"Yang Zhilin","year":"2016"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_1_45_1","unstructured":"Huishuai Zhang Wei Chen and Tie-Yan Liu. 2018a. On the local Hessian in back-propagation. NIPS . Huishuai Zhang Wei Chen and Tie-Yan Liu. 2018a. On the local Hessian in back-propagation. NIPS ."},{"key":"e_1_3_2_1_46_1","volume-title":"Graph convolution over pruned dependency trees improves relation extraction. arXiv preprint arXiv:1809.10185","author":"Zhang Yuhao","year":"2018"},{"key":"e_1_3_2_1_47_1","volume-title":"Proceedings of the 20th International conference on Machine learning (ICML-03)","author":"Zhu Xiaojin","year":"2003"},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186116"}],"event":{"name":"KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Anchorage AK USA","acronym":"KDD '19","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 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330888","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330888","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3292500.3330888","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:26:03Z","timestamp":1750206363000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3292500.3330888"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,7,25]]},"references-count":48,"alternative-id":["10.1145\/3292500.3330888","10.1145\/3292500"],"URL":"https:\/\/doi.org\/10.1145\/3292500.3330888","relation":{},"subject":[],"published":{"date-parts":[[2019,7,25]]},"assertion":[{"value":"2019-07-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}