{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,30]],"date-time":"2026-10-30T17:18:06Z","timestamp":1793380686561,"version":"4.5.0"},"publisher-location":"New York, NY, USA","reference-count":45,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,8,14]],"date-time":"2021-08-14T00:00:00Z","timestamp":1628899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,8,14]]},"DOI":"10.1145\/3447548.3467350","type":"proceedings-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T14:33:08Z","timestamp":1628865188000},"page":"1150-1160","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":307,"title":["Are we really making much progress?"],"prefix":"10.1145","author":[{"given":"Qingsong","family":"Lv","sequence":"first","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming","family":"Ding","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenzheng","family":"Feng","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siming","family":"He","sequence":"additional","affiliation":[{"name":"University of Pennsylvania, Philadelphia, PA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Zhou","sequence":"additional","affiliation":[{"name":"Alibaba Group, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianguo","family":"Jiang","sequence":"additional","affiliation":[{"name":"Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxiao","family":"Dong","sequence":"additional","affiliation":[{"name":"Microsoft, Redmond, WA, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,8,14]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"ICML'19","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija , Bryan Perozzi , Amol Kapoor , Nazanin Alipourfard , Kristina Lerman , Hrayr Harutyunyan , Greg Ver Steeg , and Aram Galstyan . 2019 . Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing . In ICML'19 . PMLR, 21--29. Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In ICML'19. PMLR, 21--29."},{"key":"e_1_3_2_2_2_1","volume-title":"et almbox","author":"Battaglia Peter W","year":"2018","unstructured":"Peter W Battaglia , Jessica B Hamrick , Victor Bapst , Alvaro Sanchez-Gonzalez , Vinicius Zambaldi , Mateusz Malinowski , Andrea Tacchetti , David Raposo , Adam Santoro , Ryan Faulkner , et almbox . 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 almbox. 2018. Relational inductive biases, deep learning, and graph networks. arXiv preprint arXiv:1806.01261 (2018)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376746"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2039320"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330964"},{"key":"e_1_3_2_2_6_1","volume-title":"Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289","author":"Clevert Djork-Arn\u00e9","year":"2015","unstructured":"Djork-Arn\u00e9 Clevert , Thomas Unterthiner , and Sepp Hochreiter . 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 ( 2015 ). Djork-Arn\u00e9 Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289 (2015)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3269206.3271768"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098036"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330673"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_2_11_1","volume-title":"NeurIPS","volume":"33","author":"Feng Wenzheng","year":"2020","unstructured":"Wenzheng Feng , Jie Zhang , Yuxiao Dong , Yu Han , Huanbo Luan , Qian Xu , Qiang Yang , Evgeny Kharlamov , and Jie Tang . 2020 . Graph Random Neural Networks for Semi-Supervised Learning on Graphs . NeurIPS , Vol. 33 (2020). Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, and Jie Tang. 2020. Graph Random Neural Networks for Semi-Supervised Learning on Graphs. NeurIPS , Vol. 33 (2020)."},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380297"},{"key":"e_1_3_2_2_13_1","volume-title":"ICML'17","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer , Samuel S Schoenholz , Patrick F Riley , Oriol Vinyals , and George E Dahl . 2017 . Neural message passing for quantum chemistry . In ICML'17 . PMLR, 1263--1272. Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In ICML'17. PMLR, 1263--1272."},{"key":"e_1_3_2_2_14_1","volume-title":"Inductive representation learning on large graphs. arXiv preprint arXiv:1706.02216","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton , Rex Ying , and Jure Leskovec . 2017. Inductive representation learning on large graphs. arXiv preprint arXiv:1706.02216 ( 2017 ). William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. arXiv preprint arXiv:1706.02216 (2017)."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_16_1","volume-title":"RealFormer: Transformer Likes Residual Attention. arXiv preprint arXiv:2012.11747","author":"He Ruining","year":"2020","unstructured":"Ruining He , Anirudh Ravula , Bhargav Kanagal , and Joshua Ainslie . 2020. RealFormer: Transformer Likes Residual Attention. arXiv preprint arXiv:2012.11747 ( 2020 ). Ruining He, Anirudh Ravula, Bhargav Kanagal, and Joshua Ainslie. 2020. RealFormer: Transformer Likes Residual Attention. arXiv preprint arXiv:2012.11747 (2020)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5833"},{"key":"e_1_3_2_2_18_1","volume-title":"2020 c. Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu , Matthias Fey , Marinka Zitnik , Yuxiao Dong , Hongyu Ren , Bowen Liu , Michele Catasta , and Jure Leskovec . 2020 c. Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687 ( 2020 ). Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 c. Open graph benchmark: Datasets for machine learning on graphs. arXiv preprint arXiv:2005.00687 (2020)."},{"key":"e_1_3_2_2_19_1","unstructured":"Ziniu Hu Yuxiao Dong Kuansan Wang Kai-Wei Chang and Yizhou Sun. 2020 b. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In KDD .  Ziniu Hu Yuxiao Dong Kuansan Wang Kai-Wei Chang and Yizhou Sun. 2020 b. GPT-GNN: Generative Pre-Training of Graph Neural Networks. In KDD ."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380027"},{"key":"e_1_3_2_2_21_1","volume-title":"ICLR'17 .","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . 2017 . Semi-supervised classification with graph convolutional networks . In ICLR'17 . Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR'17 ."},{"key":"e_1_3_2_2_22_1","volume-title":"Deepergcn: All you need to train deeper gcns. arXiv preprint arXiv:2006.07739","author":"Li Guohao","year":"2020","unstructured":"Guohao Li , Chenxin Xiong , Ali Thabet , and Bernard Ghanem . 2020 . Deepergcn: All you need to train deeper gcns. arXiv preprint arXiv:2006.07739 (2020). Guohao Li, Chenxin Xiong, Ali Thabet, and Bernard Ghanem. 2020. Deepergcn: All you need to train deeper gcns. arXiv preprint arXiv:2006.07739 (2020)."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_2_24_1","volume-title":"KRED: Knowledge-Aware Document Representation for News Recommendations. In RecSys'20 . 200--209.","author":"Liu Danyang","year":"2020","unstructured":"Danyang Liu , Jianxun Lian , Shiyin Wang , Ying Qiao , Jiun-Hung Chen , Guangzhong Sun , and Xing Xie . 2020 . KRED: Knowledge-Aware Document Representation for News Recommendations. In RecSys'20 . 200--209. Danyang Liu, Jianxun Lian, Shiyin Wang, Ying Qiao, Jiun-Hung Chen, Guangzhong Sun, and Xing Xie. 2020. KRED: Knowledge-Aware Document Representation for News Recommendations. In RecSys'20 . 200--209."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403393"},{"key":"e_1_3_2_2_26_1","volume-title":"L2-constrained softmax loss for discriminative face verification. arXiv preprint arXiv:1703.09507","author":"Ranjan Rajeev","year":"2017","unstructured":"Rajeev Ranjan , Carlos D Castillo , and Rama Chellappa . 2017. L2-constrained softmax loss for discriminative face verification. arXiv preprint arXiv:1703.09507 ( 2017 ). Rajeev Ranjan, Carlos D Castillo, and Rama Chellappa. 2017. L2-constrained softmax loss for discriminative face verification. arXiv preprint arXiv:1703.09507 (2017)."},{"key":"e_1_3_2_2_27_1","volume-title":"UAI'09","author":"Rendle Steffen","year":"2009","unstructured":"Steffen Rendle , Christoph Freudenthaler , Zeno Gantner , and Lars Schmidt-Thieme . 2009 . BPR: Bayesian personalized ranking from implicit feedback . In UAI'09 . 452--461. Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, and Lars Schmidt-Thieme. 2009. BPR: Bayesian personalized ranking from implicit feedback. In UAI'09. 452--461."},{"key":"e_1_3_2_2_28_1","volume-title":"Ivan Titov, and Max Welling.","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull , Thomas N Kipf , Peter Bloem , Rianne Van Den Berg , Ivan Titov, and Max Welling. 2018 . Modeling relational data with graph convolutional networks. In ESWC'18. Springer , 593--607. Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In ESWC'18. Springer, 593--607."},{"key":"e_1_3_2_2_29_1","volume-title":"Mining Heterogeneous Information Networks: Principles and Methodologies","author":"Sun Yizhou","unstructured":"Yizhou Sun and Jiawei Han . 2012. Mining Heterogeneous Information Networks: Principles and Methodologies . Morgan and Claypool Publishers . Yizhou Sun and Jiawei Han. 2012. Mining Heterogeneous Information Networks: Principles and Methodologies .Morgan and Claypool Publishers."},{"key":"e_1_3_2_2_30_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402736"},{"key":"e_1_3_2_2_31_1","first-page":"5998","article-title":"Attention is All you Need","volume":"30","author":"Vaswani Ashish","year":"2017","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 . NeurIPS , Vol. 30 (2017), 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. NeurIPS , Vol. 30 (2017), 5998--6008.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_32_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","author":"Petar Velivc","year":"2017","unstructured":"Petar Velivc kovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Velivc kovi\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_2_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330836"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313417"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330989"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313562"},{"key":"e_1_3_2_2_37_1","volume-title":"ICLR'18 .","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2018 a. How Powerful are Graph Neural Networks? . In ICLR'18 . Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018a. How Powerful are Graph Neural Networks?. In ICLR'18 ."},{"key":"e_1_3_2_2_38_1","volume-title":"Representation Learning on Graphs with Jumping Knowledge Networks. ICML","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Chengtao Li , Yonglong Tian , Tomohiro Sonobe , Ken-ichi Kawarabayashi, and Stefanie Jegelka . 2018b. Representation Learning on Graphs with Jumping Knowledge Networks. ICML ( 2018 ), 5449--5458. Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018b. Representation Learning on Graphs with Jumping Knowledge Networks. ICML (2018), 5449--5458."},{"key":"e_1_3_2_2_39_1","volume-title":"Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575","author":"Yang Bishan","year":"2014","unstructured":"Bishan Yang , Wen-tau Yih, Xiaodong He , Jianfeng Gao , and Li Deng . 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 ( 2014 ). Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)."},{"key":"e_1_3_2_2_40_1","volume-title":"2020 b","author":"Yang Carl","unstructured":"Carl Yang , Aditya Pal , Andrew Zhai , Nikil Pancha , Jiawei Han , Charles Rosenberg , and Jure Leskovec . 2020 b . MultiSage: Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks. In KDD '20. 2434--2443. Carl Yang, Aditya Pal, Andrew Zhai, Nikil Pancha, Jiawei Han, Charles Rosenberg, and Jure Leskovec. 2020 b. MultiSage: Empowering GCN with Contextualized Multi-Embeddings on Web-Scale Multipartite Networks. In KDD'20. 2434--2443."},{"key":"e_1_3_2_2_41_1","volume-title":"2020 c. Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark. TKDE","author":"Yang Carl","year":"2020","unstructured":"Carl Yang , Yuxin Xiao , Yu Zhang , Yizhou Sun , and Jiawei Han . 2020 c. Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark. TKDE ( 2020 ). Carl Yang, Yuxin Xiao, Yu Zhang, Yizhou Sun, and Jiawei Han. 2020 c. Heterogeneous Network Representation Learning: A Unified Framework with Survey and Benchmark. TKDE (2020)."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403218"},{"key":"e_1_3_2_2_43_1","unstructured":"Seongjun Yun Minbyul Jeong Raehyun Kim Jaewoo Kang and Hyunwoo J Kim. 2019. Graph Transformer Networks. In NeurIPS'19 .  Seongjun Yun Minbyul Jeong Raehyun Kim Jaewoo Kang and Hyunwoo J Kim. 2019. Graph Transformer Networks. In NeurIPS'19 ."},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330961"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00203"}],"event":{"name":"KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Virtual Event Singapore","acronym":"KDD '21","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 27th ACM SIGKDD Conference on Knowledge Discovery &amp; Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467350","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3447548.3467350","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:18:23Z","timestamp":1750177103000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3447548.3467350"}},"subtitle":["Revisiting, benchmarking and refining heterogeneous graph neural networks"],"short-title":[],"issued":{"date-parts":[[2021,8,14]]},"references-count":45,"alternative-id":["10.1145\/3447548.3467350","10.1145\/3447548"],"URL":"https:\/\/doi.org\/10.1145\/3447548.3467350","relation":{},"subject":[],"published":{"date-parts":[[2021,8,14]]},"assertion":[{"value":"2021-08-14","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}