{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T13:07:56Z","timestamp":1765544876522,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":63,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,10,17]],"date-time":"2022-10-17T00:00:00Z","timestamp":1665964800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF (National Science Foundation)","doi-asserted-by":"publisher","award":["IS-1703883,IIS-1955404,IIS-1955365,RETTL-2119265,EAGER-2122119"],"award-info":[{"award-number":["IS-1703883,IIS-1955404,IIS-1955365,RETTL-2119265,EAGER-2122119"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000180","name":"U.S. Department of Homeland Security","doi-asserted-by":"publisher","award":["22STESE00001 01 01"],"award-info":[{"award-number":["22STESE00001 01 01"]}],"id":[{"id":"10.13039\/100000180","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557344","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"2732-2742","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["D-HYPR"],"prefix":"10.1145","author":[{"given":"Honglu","family":"Zhou","sequence":"first","affiliation":[{"name":"Rutgers University, Piscataway, NJ, USA"}]},{"given":"Advith","family":"Chegu","sequence":"additional","affiliation":[{"name":"Rutgers University, Piscataway, NJ, USA"}]},{"given":"Samuel S.","family":"Sohn","sequence":"additional","affiliation":[{"name":"Rutgers University, Piscataway, NJ, USA"}]},{"given":"Zuohui","family":"Fu","sequence":"additional","affiliation":[{"name":"Rutgers University, Piscataway, NJ, USA"}]},{"given":"Gerard","family":"de Melo","sequence":"additional","affiliation":[{"name":"HPI \/ University of Potsdam, Potsdam, Germany"}]},{"given":"Mubbasir","family":"Kapadia","sequence":"additional","affiliation":[{"name":"Rutgers University, Piscataway, NJ, USA"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Machine Learning. PMLR, 486--496","author":"Bachmann Gregor","year":"2020","unstructured":"Gregor Bachmann , Gary B\u00e9cigneul , and Octavian Ganea . 2020 . Constant curvature graph convolutional networks . In International Conference on Machine Learning. PMLR, 486--496 . Gregor Bachmann, Gary B\u00e9cigneul, and Octavian Ganea. 2020. Constant curvature graph convolutional networks. In International Conference on Machine Learning. PMLR, 486--496."},{"key":"e_1_3_2_1_2_1","unstructured":"Peter W Battaglia Jessica B Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vinicius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner etal 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261 (2018).  Peter W Battaglia Jessica B Hamrick Victor Bapst Alvaro Sanchez-Gonzalez Vinicius Zambaldi Mateusz Malinowski Andrea Tacchetti David Raposo Adam Santoro Ryan Faulkner et al. 2018. Relational inductive biases deep learning and graph networks. arXiv preprint arXiv:1806.01261 (2018)."},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Machine Learning. PMLR, 748--758","author":"Beani Dominique","year":"2021","unstructured":"Dominique Beani , Saro Passaro , Vincent L\u00e9tourneau , Will Hamilton , Gabriele Corso , and Pietro Li\u00f2 . 2021 . Directional graph networks . In International Conference on Machine Learning. PMLR, 748--758 . Dominique Beani, Saro Passaro, Vincent L\u00e9tourneau, Will Hamilton, Gabriele Corso, and Pietro Li\u00f2. 2021. Directional graph networks. In International Conference on Machine Learning. PMLR, 748--758."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2807452"},{"key":"e_1_3_2_1_6_1","volume-title":"Neural embeddings of graphs in hyperbolic space. arXiv preprint arXiv:1705.10359","author":"Chamberlain Benjamin Paul","year":"2017","unstructured":"Benjamin Paul Chamberlain , James Clough , and Marc Peter Deisenroth . 2017. Neural embeddings of graphs in hyperbolic space. arXiv preprint arXiv:1705.10359 ( 2017 ). Benjamin Paul Chamberlain, James Clough, and Marc Peter Deisenroth. 2017. Neural embeddings of graphs in hyperbolic space. arXiv preprint arXiv:1705.10359 (2017)."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.617"},{"key":"e_1_3_2_1_8_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. 4868--4879.  Ines Chami Zhitao Ying Christopher R\u00e9 and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In Advances in neural information processing systems. 4868--4879."},{"key":"e_1_3_2_1_9_1","volume-title":"Fully Hyperbolic Neural Networks. arXiv preprint arXiv:2105.14686","author":"Chen Weize","year":"2021","unstructured":"Weize Chen , Xu Han , Yankai Lin , Hexu Zhao , Zhiyuan Liu , Peng Li , Maosong Sun , and Jie Zhou . 2021. Fully Hyperbolic Neural Networks. arXiv preprint arXiv:2105.14686 ( 2021 ). Weize Chen, Xu Han, Yankai Lin, Hexu Zhao, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2021. Fully Hyperbolic Neural Networks. arXiv preprint arXiv:2105.14686 (2021)."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00022"},{"key":"e_1_3_2_1_11_1","volume-title":"Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard , Xavier Bresson , and Pierre Vandergheynst . 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems , Vol. 29 ( 2016 ), 3844--3852. Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, Vol. 29 (2016), 3844--3852."},{"key":"e_1_3_2_1_12_1","volume-title":"International Conference on Machine Learning. PMLR, 1646--1655","author":"Ganea Octavian","year":"2018","unstructured":"Octavian Ganea , Gary B\u00e9cigneul , and Thomas Hofmann . 2018 a. Hyperbolic entailment cones for learning hierarchical embeddings . In International Conference on Machine Learning. PMLR, 1646--1655 . Octavian Ganea, Gary B\u00e9cigneul, and Thomas Hofmann. 2018a. Hyperbolic entailment cones for learning hierarchical embeddings. In International Conference on Machine Learning. PMLR, 1646--1655."},{"key":"e_1_3_2_1_13_1","unstructured":"Octavian Ganea Gary B\u00e9cigneul and Thomas Hofmann. 2018b. Hyperbolic neural networks. In Advances in neural information processing systems. 5345--5355.  Octavian Ganea Gary B\u00e9cigneul and Thomas Hofmann. 2018b. Hyperbolic neural networks. In Advances in neural information processing systems. 5345--5355."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00943"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Learning Representations.","author":"Gu Albert","year":"2018","unstructured":"Albert Gu , Frederic Sala , Beliz Gunel , and Christopher R\u00e9 . 2018 . Learning mixed-curvature representations in product spaces . In International Conference on Learning Representations. Albert Gu, Frederic Sala, Beliz Gunel, and Christopher R\u00e9. 2018. Learning mixed-curvature representations in product spaces. In International Conference on Learning Representations."},{"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":"10.1016\/j.neucom.2015.07.128"},{"key":"e_1_3_2_1_19_1","volume-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 395--411","author":"Khosla Megha","year":"2019","unstructured":"Megha Khosla , Jurek Leonhardt , Wolfgang Nejdl , and Avishek Anand . 2019 . Node representation learning for directed graphs . In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 395--411 . Megha Khosla, Jurek Leonhardt, Wolfgang Nejdl, and Avishek Anand. 2019. Node representation learning for directed graphs. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, 395--411."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186117"},{"key":"e_1_3_2_1_21_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_1_22_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016a. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 ( 2016 ). Thomas N Kipf and Max Welling. 2016a. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_23_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 ( 2016 ). Thomas N Kipf and Max Welling. 2016b. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_1_24_1","volume-title":"Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997","author":"Klicpera Johannes","year":"2018","unstructured":"Johannes Klicpera , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2018. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997 ( 2018 ). Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Predict then propagate: Graph neural networks meet personalized pagerank. arXiv preprint arXiv:1810.05997 (2018)."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-019-0195-3"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.82.036106"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.80.035101"},{"key":"e_1_3_2_1_28_1","volume-title":"Ultrahyperbolic Representation Learning. Advances in neural information processing systems (NeurIPS 2020)","author":"Law Marc T","year":"2020","unstructured":"Marc T Law and Jos Stam . 2020. Ultrahyperbolic Representation Learning. Advances in neural information processing systems (NeurIPS 2020) ( 2020 ). Marc T Law and Jos Stam. 2020. Ultrahyperbolic Representation Learning. Advances in neural information processing systems (NeurIPS 2020) (2020)."},{"key":"e_1_3_2_1_29_1","volume-title":"Hyperbolic graph neural networks. arXiv preprint arXiv:1910.12892","author":"Liu Qi","year":"2019","unstructured":"Qi Liu , Maximilian Nickel , and Douwe Kiela . 2019. Hyperbolic graph neural networks. arXiv preprint arXiv:1910.12892 ( 2019 ). Qi Liu, Maximilian Nickel, and Douwe Kiela. 2019. Hyperbolic graph neural networks. arXiv preprint arXiv:1910.12892 (2019)."},{"key":"e_1_3_2_1_30_1","volume-title":"International Conference on Machine Learning. PMLR, 4212--4221","author":"Ma Jianxin","year":"2019","unstructured":"Jianxin Ma , Peng Cui , Kun Kuang , Xin Wang , and Wenwu Zhu . 2019 a. Disentangled graph convolutional networks . In International Conference on Machine Learning. PMLR, 4212--4221 . Jianxin Ma, Peng Cui, Kun Kuang, Xin Wang, and Wenwu Zhu. 2019a. Disentangled graph convolutional networks. In International Conference on Machine Learning. PMLR, 4212--4221."},{"key":"e_1_3_2_1_31_1","volume-title":"Spectral-based graph convolutional network for directed graphs. arXiv preprint arXiv:1907.08990","author":"Ma Yi","year":"2019","unstructured":"Yi Ma , Jianye Hao , Yaodong Yang , Han Li , Junqi Jin , and Guangyong Chen . 2019b. Spectral-based graph convolutional network for directed graphs. arXiv preprint arXiv:1907.08990 ( 2019 ). Yi Ma, Jianye Hao, Yaodong Yang, Han Li, Junqi Jin, and Guangyong Chen. 2019b. Spectral-based graph convolutional network for directed graphs. arXiv preprint arXiv:1907.08990 (2019)."},{"key":"e_1_3_2_1_32_1","volume-title":"Birds of a feather: Homophily in social networks. Annual review of sociology","author":"McPherson Miller","year":"2001","unstructured":"Miller McPherson , Lynn Smith-Lovin , and James M Cook . 2001. Birds of a feather: Homophily in social networks. Annual review of sociology , Vol. 27 , 1 ( 2001 ), 415--444. Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology, Vol. 27, 1 (2001), 415--444."},{"key":"e_1_3_2_1_33_1","volume-title":"A brief history of generative models for power law and lognormal distributions. Internet mathematics","author":"Mitzenmacher Michael","year":"2004","unstructured":"Michael Mitzenmacher . 2004. A brief history of generative models for power law and lognormal distributions. Internet mathematics , Vol. 1 , 2 ( 2004 ), 226--251. Michael Mitzenmacher. 2004. A brief history of generative models for power law and lognormal distributions. Internet mathematics, Vol. 1, 2 (2004), 226--251."},{"key":"e_1_3_2_1_34_1","volume-title":"Advances in neural information processing systems (NeurIPS","author":"Nickel Maximillian","year":"2017","unstructured":"Maximillian Nickel and Douwe Kiela . 2017. Poincar\u00e9 embeddings for learning hierarchical representations . In Advances in neural information processing systems (NeurIPS 2017 ). 6338--6347. Maximillian Nickel and Douwe Kiela. 2017. Poincar\u00e9 embeddings for learning hierarchical representations. In Advances in neural information processing systems (NeurIPS 2017). 6338--6347."},{"key":"e_1_3_2_1_35_1","volume-title":"International Conference on Machine Learning. PMLR, 3779--3788","author":"Nickel Maximillian","year":"2018","unstructured":"Maximillian Nickel and Douwe Kiela . 2018 . Learning continuous hierarchies in the lorentz model of hyperbolic geometry . In International Conference on Machine Learning. PMLR, 3779--3788 . Maximillian Nickel and Douwe Kiela. 2018. Learning continuous hierarchies in the lorentz model of hyperbolic geometry. In International Conference on Machine Learning. PMLR, 3779--3788."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939751"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_38_1","volume-title":"SIGN: Scalable Inception Graph Neural Networks. arXiv preprint arXiv:2004.11198","author":"Rossi Emanuele","year":"2020","unstructured":"Emanuele Rossi , Fabrizio Frasca , Ben Chamberlain , Davide Eynard , Michael Bronstein , and Federico Monti . 2020 . SIGN: Scalable Inception Graph Neural Networks. arXiv preprint arXiv:2004.11198 (2020). Emanuele Rossi, Fabrizio Frasca, Ben Chamberlain, Davide Eynard, Michael Bronstein, and Federico Monti. 2020. SIGN: Scalable Inception Graph Neural Networks. arXiv preprint arXiv:2004.11198 (2020)."},{"key":"e_1_3_2_1_39_1","volume-title":"International conference on machine learning. PMLR, 4460--4469","author":"Sala Frederic","year":"2018","unstructured":"Frederic Sala , Chris De Sa , Albert Gu , and Christopher R\u00e9 . 2018 . Representation tradeoffs for hyperbolic embeddings . In International conference on machine learning. PMLR, 4460--4469 . Frederic Sala, Chris De Sa, Albert Gu, and Christopher R\u00e9. 2018. Representation tradeoffs for hyperbolic embeddings. In International conference on machine learning. PMLR, 4460--4469."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3357384.3358023"},{"key":"e_1_3_2_1_41_1","article-title":"Weisfeiler-lehman graph kernels","volume":"12","author":"Shervashidze Nino","year":"2011","unstructured":"Nino Shervashidze , Pascal Schweitzer , Erik Jan Van Leeuwen , Kurt Mehlhorn , and Karsten M Borgwardt . 2011 . Weisfeiler-lehman graph kernels . Journal of Machine Learning Research , Vol. 12 , 9 (2011). Nino Shervashidze, Pascal Schweitzer, Erik Jan Van Leeuwen, Kurt Mehlhorn, and Karsten M Borgwardt. 2011. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research, Vol. 12, 9 (2011).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00810"},{"key":"e_1_3_2_1_43_1","volume-title":"International Conference on Machine Learning. PMLR, 9681--9690","author":"Sim Aaron","year":"2021","unstructured":"Aaron Sim , Maciej L Wiatrak , Angus Brayne , P\u00e1id'i Creed , and Saee Paliwal . 2021 . Directed graph embeddings in pseudo-riemannian manifolds . In International Conference on Machine Learning. PMLR, 9681--9690 . Aaron Sim, Maciej L Wiatrak, Angus Brayne, P\u00e1id'i Creed, and Saee Paliwal. 2021. Directed graph embeddings in pseudo-riemannian manifolds. In International Conference on Machine Learning. PMLR, 9681--9690."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301265"},{"key":"e_1_3_2_1_45_1","volume-title":"International Conference on Machine Learning. PMLR, 6066--6075","author":"Suzuki Ryota","year":"2019","unstructured":"Ryota Suzuki , Ryusuke Takahama , and Shun Onoda . 2019 . Hyperbolic disk embeddings for directed acyclic graphs . In International Conference on Machine Learning. PMLR, 6066--6075 . Ryota Suzuki, Ryusuke Takahama, and Shun Onoda. 2019. Hyperbolic disk embeddings for directed acyclic graphs. In International Conference on Machine Learning. PMLR, 6066--6075."},{"key":"e_1_3_2_1_46_1","volume-title":"Directed Acyclic Graph Neural Networks. International Conference on Learning Representations","author":"Thost Veronika","year":"2021","unstructured":"Veronika Thost and Jie Chen . 2021 . Directed Acyclic Graph Neural Networks. International Conference on Learning Representations (2021). Veronika Thost and Jie Chen. 2021. Directed Acyclic Graph Neural Networks. International Conference on Learning Representations (2021)."},{"key":"e_1_3_2_1_47_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS)","volume":"34","author":"Tong Zekun","year":"2021","unstructured":"Zekun Tong , Yuxuan Liang , Henghui Ding , Yongxing Dai , Xinke Li , and Changhu Wang . 2021 . Directed Graph Contrastive Learning . Advances in Neural Information Processing Systems (NeurIPS) , Vol. 34 (2021). Zekun Tong, Yuxuan Liang, Henghui Ding, Yongxing Dai, Xinke Li, and Changhu Wang. 2021. Directed Graph Contrastive Learning. Advances in Neural Information Processing Systems (NeurIPS), Vol. 34 (2021)."},{"key":"e_1_3_2_1_48_1","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Tong Zekun","year":"2020","unstructured":"Zekun Tong , Yuxuan Liang , Changsheng Sun , Xinke Li , David Rosenblum , and Andrew Lim . 2020 a. Digraph Inception Convolutional Networks . Advances in Neural Information Processing Systems , Vol. 33 (2020). Zekun Tong, Yuxuan Liang, Changsheng Sun, Xinke Li, David Rosenblum, and Andrew Lim. 2020a. Digraph Inception Convolutional Networks. Advances in Neural Information Processing Systems, Vol. 33 (2020)."},{"key":"e_1_3_2_1_49_1","volume-title":"Directed Graph Convolutional Network. arXiv preprint arXiv:2004.13970","author":"Tong Zekun","year":"2020","unstructured":"Zekun Tong , Yuxuan Liang , Changsheng Sun , David S Rosenblum , and Andrew Lim . 2020b. Directed Graph Convolutional Network. arXiv preprint arXiv:2004.13970 ( 2020 ). Zekun Tong, Yuxuan Liang, Changsheng Sun, David S Rosenblum, and Andrew Lim. 2020b. Directed Graph Convolutional Network. arXiv preprint arXiv:2004.13970 (2020)."},{"key":"e_1_3_2_1_50_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_51_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Veliv\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veliv\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Veliv\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_1_52_1","volume-title":"Deep graph infomax. arXiv preprint arXiv:1809.10341","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107, William Fedus , William L Hamilton , Pietro Li\u00f2 , Yoshua Bengio , and R Devon Hjelm . 2018. Deep graph infomax. arXiv preprint arXiv:1809.10341 ( 2018 ). Petar Velivc kovi\u0107, William Fedus, William L Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R Devon Hjelm. 2018. Deep graph infomax. arXiv preprint arXiv:1809.10341 (2018)."},{"key":"e_1_3_2_1_53_1","volume-title":"Principal component analysis. Chemometrics and intelligent laboratory systems","author":"Wold Svante","year":"1987","unstructured":"Svante Wold , Kim Esbensen , and Paul Geladi . 1987. Principal component analysis. Chemometrics and intelligent laboratory systems , Vol. 2 , 1--3 ( 1987 ), 37--52. Svante Wold, Kim Esbensen, and Paul Geladi. 1987. Principal component analysis. Chemometrics and intelligent laboratory systems, Vol. 2, 1--3 (1987), 37--52."},{"key":"e_1_3_2_1_54_1","volume-title":"Christopher Fifty, Tao Yu, and Kilian Q Weinberger.","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Tianyi Zhang , Amauri Holanda de Souza Jr , Christopher Fifty, Tao Yu, and Kilian Q Weinberger. 2019 . Simplifying graph convolutional networks. arXiv preprint arXiv:1902.07153 (2019). Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr, Christopher Fifty, Tao Yu, and Kilian Q Weinberger. 2019. Simplifying graph convolutional networks. arXiv preprint arXiv:1902.07153 (2019)."},{"key":"e_1_3_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_1_56_1","doi-asserted-by":"crossref","unstructured":"Cheng Yang Maosong Sun Zhiyuan Liu and Cunchao Tu. 2017. Fast network embedding enhancement via high order proximity approximation. In IJCAI. 3894--3900.  Cheng Yang Maosong Sun Zhiyuan Liu and Cunchao Tu. 2017. Fast network embedding enhancement via high order proximity approximation. In IJCAI. 3894--3900.","DOI":"10.24963\/ijcai.2017\/544"},{"key":"e_1_3_2_1_57_1","volume-title":"Do Transformers Really Perform Bad for Graph Representation?","author":"Ying Chengxuan","year":"2021","unstructured":"Chengxuan Ying , Tianle Cai , Shengjie Luo , Shuxin Zheng , Guolin Ke , Di He , Yanming Shen , and Tie-Yan Liu . 2021. Do Transformers Really Perform Bad for Graph Representation? ( 2021 ). Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu. 2021. Do Transformers Really Perform Bad for Graph Representation? (2021)."},{"key":"e_1_3_2_1_58_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"Zhang Xitong","year":"2021","unstructured":"Xitong Zhang , Nathan Brugnone , Michael Perlmutter , and Matthew Hirn . 2021 a. MagNet: A Magnetic Neural Network for Directed Graphs . Advances in Neural Information Processing Systems , Vol. 34 (2021). Xitong Zhang, Nathan Brugnone, Michael Perlmutter, and Matthew Hirn. 2021a. MagNet: A Magnetic Neural Network for Directed Graphs. Advances in Neural Information Processing Systems, Vol. 34 (2021)."},{"key":"e_1_3_2_1_59_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2021.3081431"},{"key":"e_1_3_2_1_60_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449872"},{"key":"e_1_3_2_1_61_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v31i1.10878"},{"key":"e_1_3_2_1_62_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_2_1_63_1","unstructured":"Shichao Zhu Shirui Pan Chuan Zhou Jia Wu Yanan Cao and Bin Wang. 2020. Graph Geometry Interaction Learning. In Advances in Neural Information Processing Systems.  Shichao Zhu Shirui Pan Chuan Zhou Jia Wu Yanan Cao and Bin Wang. 2020. Graph Geometry Interaction Learning. In Advances in Neural Information Processing Systems."}],"event":{"name":"CIKM '22: The 31st 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":"Atlanta GA USA","acronym":"CIKM '22"},"container-title":["Proceedings of the 31st ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557344","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557344","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557344","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:49:29Z","timestamp":1750182569000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557344"}},"subtitle":["Harnessing Neighborhood Modeling and Asymmetry Preservation for Digraph Representation Learning"],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":63,"alternative-id":["10.1145\/3511808.3557344","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557344","relation":{},"subject":[],"published":{"date-parts":[[2022,10,17]]},"assertion":[{"value":"2022-10-17","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}