{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T12:40:59Z","timestamp":1688647259534},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020AAA0107600"]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,7,6]]},"DOI":"10.1145\/3477495.3531804","type":"proceedings-article","created":{"date-parts":[[2022,7,7]],"date-time":"2022-07-07T15:12:08Z","timestamp":1657206728000},"update-policy":"http:\/\/dx.doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["BSAL"],"prefix":"10.1145","author":[{"given":"Bisheng","family":"Li","sequence":"first","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Min","family":"Zhou","sequence":"additional","affiliation":[{"name":"Huawei Noah's Ark Lab, Shenzhen, China"}]},{"given":"Shengzhong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]},{"given":"Menglin","family":"Yang","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong, Hong Kong, China"}]},{"given":"Defu","family":"Lian","sequence":"additional","affiliation":[{"name":"University of Science and Technology of China, Hefei, China"}]},{"given":"Zengfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Fudan University, Shanghai, China"}]}],"member":"320","published-online":{"date-parts":[[2022,7,7]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Friends and neighbors on the web. Social networks 25, 3","author":"Adamic Lada A","year":"2003","unstructured":"Lada A Adamic and Eytan Adar . 2003. Friends and neighbors on the web. Social networks 25, 3 ( 2003 ), 211--230. Lada A Adamic and Eytan Adar. 2003. Friends and neighbors on the web. Social networks 25, 3 (2003), 211--230."},{"key":"e_1_3_2_2_2_1","volume-title":"Emergence of scaling in random networks. science 286, 5439","author":"Barab\u00e1si Albert-L\u00e1szl\u00f3","year":"1999","unstructured":"Albert-L\u00e1szl\u00f3 Barab\u00e1si and R\u00e9ka Albert . 1999. Emergence of scaling in random networks. science 286, 5439 ( 1999 ), 509--512. Albert-L\u00e1szl\u00f3 Barab\u00e1si and R\u00e9ka Albert. 1999. Emergence of scaling in random networks. science 286, 5439 (1999), 509--512."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3080635"},{"key":"e_1_3_2_2_4_1","unstructured":"Ines Chami Zhitao Ying Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In Advances in Neural Information Processing Systems. 4869--4880. Ines Chami Zhitao Ying Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In Advances in Neural Information Processing Systems. 4869--4880."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1017\/S1351324916000334"},{"key":"e_1_3_2_2_6_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","unstructured":"Matthias Fey and Jan E. Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1088\/0256-307X\/22\/2\/068"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467256"},{"key":"e_1_3_2_2_10_1","volume-title":"Variational Graph Auto-Encoders. NIPS Workshop on Bayesian Deep Learning","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016 . Variational Graph Auto-Encoders. NIPS Workshop on Bayesian Deep Learning (2016). Thomas N Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. NIPS Workshop on Bayesian Deep Learning (2016)."},{"key":"e_1_3_2_2_11_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403252"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623638"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.5555\/1241540.1241551"},{"key":"e_1_3_2_2_16_1","volume-title":"Enhancing Hyperbolic Graph Embeddings via Contrastive Learning. arXiv preprint arXiv:2201.08554","author":"Liu Jiahong","year":"2022","unstructured":"Jiahong Liu , Menglin Yang , Min Zhou , Shanshan Feng , and Philippe FournierViger . 2022. Enhancing Hyperbolic Graph Embeddings via Contrastive Learning. arXiv preprint arXiv:2201.08554 ( 2022 ). Jiahong Liu, Menglin Yang, Min Zhou, Shanshan Feng, and Philippe FournierViger. 2022. Enhancing Hyperbolic Graph Embeddings via Contrastive Learning. arXiv preprint arXiv:2201.08554 (2022)."},{"key":"e_1_3_2_2_17_1","volume-title":"Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS computational biology 12, 2","author":"Liu Yong","year":"2016","unstructured":"Yong Liu , Min Wu , Chunyan Miao , Peilin Zhao , and Xiao-Li Li. 2016. Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS computational biology 12, 2 ( 2016 ), e1004760. Yong Liu, Min Wu, Chunyan Miao, Peilin Zhao, and Xiao-Li Li. 2016. Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS computational biology 12, 2 (2016), e1004760."},{"key":"e_1_3_2_2_18_1","volume-title":"DensE: An Enhanced Noncommutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy. Neurocomputing","author":"Lu Haonan","year":"2022","unstructured":"Haonan Lu , Hailin Hu , and Xiaodong Lin . 2022. DensE: An Enhanced Noncommutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy. Neurocomputing ( 2022 ). Haonan Lu, Hailin Hu, and Xiaodong Lin. 2022. DensE: An Enhanced Noncommutative Representation for Knowledge Graph Embedding with Adaptive Semantic Hierarchy. Neurocomputing (2022)."},{"key":"e_1_3_2_2_19_1","volume-title":"Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390, 6","author":"L\u00fc Linyuan","year":"2011","unstructured":"Linyuan L\u00fc and Tao Zhou . 2011. Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390, 6 ( 2011 ), 1150--1170. Linyuan L\u00fc and Tao Zhou. 2011. Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications 390, 6 (2011), 1150--1170."},{"key":"e_1_3_2_2_20_1","volume-title":"Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. Briefings in Bioinformatics","author":"Ou-Yang Le","year":"2021","unstructured":"Le Ou-Yang , Fan Lu , Zi-Chao Zhang , and Min Wu. 2021. Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. Briefings in Bioinformatics ( 2021 ). Le Ou-Yang, Fan Lu, Zi-Chao Zhang, and Min Wu. 2021. Matrix factorization for biomedical link prediction and scRNA-seq data imputation: an empirical survey. Briefings in Bioinformatics (2021)."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"e_1_3_2_2_23_1","unstructured":"Oleksandr Shchur Maximilian Mumme Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Pitfalls of Graph Neural Network Evaluation. (2018). Oleksandr Shchur Maximilian Mumme Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Pitfalls of Graph Neural Network Evaluation. (2018)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_2_25_1","volume-title":"Graph Attention Networks. International Conference on Learning Representations","author":"Cucurull Guillem","year":"2018","unstructured":"Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018 . Graph Attention Networks. International Conference on Learning Representations (2018). accepted as poster. Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. International Conference on Learning Representations (2018). accepted as poster."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3463913","article-title":"HyperSoRec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation","volume":"40","author":"Wang Hao","year":"2021","unstructured":"Hao Wang , Defu Lian , Hanghang Tong , Qi Liu , Zhenya Huang , and Enhong Chen . 2021 . HyperSoRec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation . ACM Transactions on Information Systems (TOIS) 40 , 2 (2021), 1 -- 28 . Hao Wang, Defu Lian, Hanghang Tong, Qi Liu, Zhenya Huang, and Enhong Chen. 2021. HyperSoRec: Exploiting hyperbolic user and item representations with multiple aspects for social-aware recommendation. ACM Transactions on Information Systems (TOIS) 40, 2 (2021), 1--28.","journal-title":"ACM Transactions on Information Systems (TOIS)"},{"key":"e_1_3_2_2_27_1","volume-title":"Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1317--1323","author":"Wang Hanchen","year":"2021","unstructured":"Hanchen Wang , Defu Lian , Ying Zhang , Lu Qin , and Xuemin Lin . 2021 . GoGNN: graph of graphs neural network for predicting structured entity interactions . In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1317--1323 . Hanchen Wang, Defu Lian, Ying Zhang, Lu Qin, and Xuemin Lin. 2021. GoGNN: graph of graphs neural network for predicting structured entity interactions. In Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence. 1317--1323."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403108"},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"},{"key":"e_1_3_2_2_30_1","volume-title":"Friendbook: a semantic-based friend recommendation system for social networks","author":"Wang Zhibo","year":"2014","unstructured":"Zhibo Wang , Jilong Liao , Qing Cao , Hairong Qi , and Zhi Wang . 2014. Friendbook: a semantic-based friend recommendation system for social networks . IEEE transactions on mobile computing 14, 3 ( 2014 ), 538--551. Zhibo Wang, Jilong Liao, Qing Cao, Hairong Qi, and Zhi Wang. 2014. Friendbook: a semantic-based friend recommendation system for social networks. IEEE transactions on mobile computing 14, 3 (2014), 538--551."},{"key":"e_1_3_2_2_31_1","volume-title":"The SIR model and the foundations of public health. Materials matematics","author":"Weiss Howard Howie","year":"2013","unstructured":"Howard Howie Weiss . 2013. The SIR model and the foundations of public health. Materials matematics ( 2013 ), 0001--17. Howard Howie Weiss. 2013. The SIR model and the foundations of public health. Materials matematics (2013), 0001--17."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403118"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM50108.2020.00082"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467422"},{"key":"e_1_3_2_2_35_1","volume-title":"Hyperbolic Graph Neural Networks: A Review of Methods and Applications. arXiv preprint arXiv:2202.13852","author":"Yang Menglin","year":"2022","unstructured":"Menglin Yang , Min Zhou , Zhihao Li , Jiahong Liu , Lujia Pan , Hui Xiong , and Irwin King . 2022. Hyperbolic Graph Neural Networks: A Review of Methods and Applications. arXiv preprint arXiv:2202.13852 ( 2022 ). Menglin Yang, Min Zhou, Zhihao Li, Jiahong Liu, Lujia Pan, Hui Xiong, and Irwin King. 2022. Hyperbolic Graph Neural Networks: A Review of Methods and Applications. arXiv preprint arXiv:2202.13852 (2022)."},{"key":"e_1_3_2_2_36_1","volume-title":"HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization. In TheWebConf.","author":"Yang Menglin","year":"2022","unstructured":"Menglin Yang , Min Zhou , Jiahong Liu , Defu Lian , and Irwin King . 2022 . HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization. In TheWebConf. Menglin Yang, Min Zhou, Jiahong Liu, Defu Lian, and Irwin King. 2022. HRCF: Enhancing Collaborative Filtering via Hyperbolic Geometric Regularization. In TheWebConf."},{"key":"e_1_3_2_2_37_1","volume-title":"International conference on machine learning. PMLR, 40--48","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang , William Cohen , and Ruslan Salakhudinov . 2016 . Revisiting semisupervised learning with graph embeddings . In International conference on machine learning. PMLR, 40--48 . Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting semisupervised learning with graph embeddings. In International conference on machine learning. PMLR, 40--48."},{"key":"e_1_3_2_2_38_1","volume-title":"Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction. Advances in Neural Information Processing Systems 34","author":"Yun Seongjun","year":"2021","unstructured":"Seongjun Yun , Seoyoon Kim , Junhyun Lee , Jaewoo Kang , and Hyunwoo J Kim . 2021. Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction. Advances in Neural Information Processing Systems 34 ( 2021 ). Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, and Hyunwoo J Kim. 2021. Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Ethan Zhang and Yi Zhang. 2009. Average Precision. Springer US Boston MA 192--193. Ethan Zhang and Yi Zhang. 2009. Average Precision. Springer US Boston MA 192--193.","DOI":"10.1007\/978-0-387-39940-9_482"},{"key":"e_1_3_2_2_40_1","first-page":"5165","article-title":"Link prediction based on graph neural networks","volume":"31","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen . 2018 . Link prediction based on graph neural networks . Advances in Neural Information Processing Systems 31 (2018), 5165 -- 5175 . Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in Neural Information Processing Systems 31 (2018), 5165-- 5175.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_41_1","volume-title":"Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. Advances in Neural Information Processing Systems 34","author":"Zhang Muhan","year":"2021","unstructured":"Muhan Zhang , Pan Li , Yinglong Xia , Kai Wang , and Long Jin . 2021 . Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. Advances in Neural Information Processing Systems 34 (2021). Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin. 2021. Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_42_1","volume-title":"Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. Advances in Neural Information Processing Systems 34","author":"Zhang Zaixi","year":"2021","unstructured":"Zaixi Zhang , Qi Liu , Hao Wang , Chengqiang Lu , and Chee-Kong Lee . 2021. Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. Advances in Neural Information Processing Systems 34 ( 2021 ). Zaixi Zhang, Qi Liu, Hao Wang, Chengqiang Lu, and Chee-Kong Lee. 2021. Motif-based Graph Self-Supervised Learning for Molecular Property Prediction. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_43_1","volume-title":"Cone: Cone embeddings for multi-hop reasoning over knowledge graphs. Advances in Neural Information Processing Systems 34","author":"Zhang Zhanqiu","year":"2021","unstructured":"Zhanqiu Zhang , Jie Wang , Jiajun Chen , Shuiwang Ji , and Feng Wu . 2021 . Cone: Cone embeddings for multi-hop reasoning over knowledge graphs. Advances in Neural Information Processing Systems 34 (2021). Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, and Feng Wu. 2021. Cone: Cone embeddings for multi-hop reasoning over knowledge graphs. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1140\/epjb\/e2009-00335-8"}],"event":{"name":"SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval","location":"Madrid Spain","acronym":"SIGIR '22","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3477495.3531804","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T12:02:25Z","timestamp":1688644945000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3477495.3531804"}},"subtitle":["A Framework of Bi-component Structure and Attribute Learning for Link Prediction"],"short-title":[],"issued":{"date-parts":[[2022,7,6]]},"references-count":44,"alternative-id":["10.1145\/3477495.3531804","10.1145\/3477495"],"URL":"http:\/\/dx.doi.org\/10.1145\/3477495.3531804","relation":{},"subject":[],"published":{"date-parts":[[2022,7,6]]},"assertion":[{"value":"2022-07-07","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}