{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T15:00:21Z","timestamp":1773154821322,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,3,5]],"date-time":"2021-03-05T00:00:00Z","timestamp":1614902400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2018YFA0701603"],"award-info":[{"award-number":["2018YFA0701603"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2008085MF213"],"award-info":[{"award-number":["2008085MF213"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,3,5]]},"DOI":"10.1145\/3461353.3461390","type":"proceedings-article","created":{"date-parts":[[2021,9,6]],"date-time":"2021-09-06T17:21:15Z","timestamp":1630948875000},"page":"202-207","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["DGVAE:An End-to-end Model for Link Prediction in Directed Graphs"],"prefix":"10.1145","author":[{"given":"Chensheng","family":"Li","sequence":"first","affiliation":[{"name":"University of Science and Technology of China, China"}]},{"given":"Xiaowei","family":"Qin","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, China"}]},{"given":"Dujia","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, China"}]},{"given":"Guo","family":"Wei","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, China"}]}],"member":"320","published-online":{"date-parts":[[2021,9,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999792.2999923"},{"key":"e_1_3_2_1_2_1","unstructured":"I Chami S Abu-El-Haija and B Perozzi\u00a0et al. 2020. Machine learning on graphs: A model and comprehensive taxonomy. arXiv preprint arXiv:2005(2020).  I Chami S Abu-El-Haija and B Perozzi\u00a0et al. 2020. Machine learning on graphs: A model and comprehensive taxonomy. arXiv preprint arXiv:2005(2020)."},{"key":"e_1_3_2_1_3_1","first-page":"105631","article-title":"Scalable graph convolutional networks with fast localized spectral filter for directed graphs","volume":"8","author":"Chensheng Li","year":"2020","unstructured":"Li Chensheng , Qin Xiaowei , and Xu Xiaodong\u00a0et al. 2020 . Scalable graph convolutional networks with fast localized spectral filter for directed graphs . IEEE Access 8 (2020), 105631 \u2013 105644 . Li Chensheng, Qin Xiaowei, and Xu Xiaodong\u00a0et al. 2020. Scalable graph convolutional networks with fast localized spectral filter for directed graphs. IEEE Access 8(2020), 105631\u2013105644.","journal-title":"IEEE Access"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330925"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00026-005-0237-z"},{"key":"e_1_3_2_1_6_1","volume-title":"Spectral graph theory. American Mathematical Soc.92","author":"Chung RK","year":"1997","unstructured":"Fan\u00a0 RK Chung and Fan\u00a0Chung Graham . 1997. Spectral graph theory. American Mathematical Soc.92 ( 1997 ). Fan\u00a0RK Chung and Fan\u00a0Chung Graham. 1997. Spectral graph theory. American Mathematical Soc.92 (1997)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3157382.3157527"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969442.2969488"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_11_1","volume-title":"Deep convolutional networks on graph-structured data. arXiv:1506.05163","author":"Henaff Mikael","year":"2015","unstructured":"Mikael Henaff , Joan Bruna , and Yann LeCun . 2015. Deep convolutional networks on graph-structured data. arXiv:1506.05163 ( 2015 ). Mikael Henaff, Joan Bruna, and Yann LeCun. 2015. Deep convolutional networks on graph-structured data. arXiv:1506.05163 (2015)."},{"key":"e_1_3_2_1_12_1","volume-title":"Adam: A method for stochastic optimization. arXiv:1412.6980","author":"Kingma P","year":"2014","unstructured":"Diederik\u00a0 P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv:1412.6980 (2014). Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_13_1","volume-title":"Auto-Encoding Variational Bayes. stat 1050, 1","author":"Kingma D\u00a0P","year":"2014","unstructured":"D\u00a0P Kingma and M Welling . 2014. Auto-Encoding Variational Bayes. stat 1050, 1 ( 2014 ). D\u00a0P Kingma and M Welling. 2014. Auto-Encoding Variational Bayes. stat 1050, 1 (2014)."},{"key":"e_1_3_2_1_14_1","volume-title":"International Conference on Learning Representations.","author":"Kipf N","year":"2017","unstructured":"Thomas\u00a0 N Kipf and Max Welling . 2017 . Semi-supervised classification with graph convolutional networks . In International Conference on Learning Representations. Thomas\u00a0N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_15_1","volume-title":"NIPS workshop.","author":"Kipf T\u00a0N","year":"2017","unstructured":"T\u00a0N Kipf and M Welling . 2017 . Variational graph auto-encoders . In NIPS workshop. T\u00a0N Kipf and M Welling. 2017. Variational graph auto-encoders. In NIPS workshop."},{"key":"e_1_3_2_1_16_1","volume-title":"Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications 553","author":"Kumar A","year":"2020","unstructured":"A Kumar , S\u00a0S Singh , and K Singh\u00a0et al. 2020. Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications 553 ( 2020 ), 124289. A Kumar, S\u00a0S Singh, and K Singh\u00a0et al. 2020. Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications 553 (2020), 124289."},{"key":"e_1_3_2_1_17_1","volume-title":"International Conference on Machine Learning.","author":"Lacroix T","year":"2018","unstructured":"T Lacroix , N Usunier , and G Obozinski . 2018 . Canonical tensor decomposition for knowledge base completion . In International Conference on Machine Learning. T Lacroix, N Usunier, and G Obozinski. 2018. Canonical tensor decomposition for knowledge base completion. In International Conference on Machine Learning."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219980"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611975321.18"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.5555\/2886521.2886624"},{"key":"e_1_3_2_1_21_1","volume-title":"GeniePath: Graph Neural Networks with Adaptive Receptive Paths. In AAAI Conference on Artificial Intelligence.","author":"Liu Ziqi","year":"2018","unstructured":"Ziqi Liu , Chaochao Chen , Longfei Li , Jun Zhou , Xiaolong Li , Le Song , and Yuan Qi . 2018 . GeniePath: Graph Neural Networks with Adaptive Receptive Paths. In AAAI Conference on Artificial Intelligence. Ziqi Liu, Chaochao Chen, Longfei Li, Jun Zhou, Xiaolong Li, Le Song, and Yuan Qi. 2018. GeniePath: Graph Neural Networks with Adaptive Receptive Paths. In AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939751"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301265"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045609"},{"key":"e_1_3_2_1_28_1","volume-title":"International Conference on Learning Representation.","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018 . Graph attention networks . In International Conference on Learning Representation. Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In International Conference on Learning Representation."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2754499"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.5555\/2893873.2894046"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_32_1","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence.","author":"Xiao H","year":"2015","unstructured":"H Xiao , M Huang , and Y Hao\u00a0et al. 2015 . TransA: An adaptive approach for knowledge graph embedding . In Proceedings of the AAAI Conference on Artificial Intelligence. H Xiao, M Huang, and Y Hao\u00a0et al. 2015. TransA: An adaptive approach for knowledge graph embedding. In Proceedings of the AAAI Conference on Artificial Intelligence."},{"key":"e_1_3_2_1_33_1","volume-title":"International Conference on Learning Representation.","author":"Yang B","year":"2014","unstructured":"B Yang , W Yih , and X He\u00a0et al. 2014 . Embedding entities and relations for learning and inference in knowledge bases . In International Conference on Learning Representation. B Yang, W Yih, and X He\u00a0et al. 2014. Embedding entities and relations for learning and inference in knowledge bases. In International Conference on Learning Representation."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327345.3327389"}],"event":{"name":"ICIAI 2021: 2021 the 5th International Conference on Innovation in Artificial Intelligence","location":"Xia men China","acronym":"ICIAI 2021"},"container-title":["2021 the 5th International Conference on Innovation in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3461353.3461390","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3461353.3461390","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:28:35Z","timestamp":1750195715000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3461353.3461390"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,5]]},"references-count":34,"alternative-id":["10.1145\/3461353.3461390","10.1145\/3461353"],"URL":"https:\/\/doi.org\/10.1145\/3461353.3461390","relation":{},"subject":[],"published":{"date-parts":[[2021,3,5]]},"assertion":[{"value":"2021-09-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}