{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:03:29Z","timestamp":1750309409791,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSF","award":["III-2106758"],"award-info":[{"award-number":["III-2106758"]}]},{"name":"NSF-POSE","award":["2346158"],"award-info":[{"award-number":["2346158"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679811","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:11Z","timestamp":1729452851000},"page":"3145-3154","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Revisit Orthogonality in Graph-Regularized MLPs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-1330-0899","authenticated-orcid":false,"given":"Hengrui","family":"Zhang","sequence":"first","affiliation":[{"name":"University of Illinois at Chicago, Chicago, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9086-7709","authenticated-orcid":false,"given":"Shen","family":"Wang","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Santa Clara, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8367-0733","authenticated-orcid":false,"given":"Vassilis N.","family":"Ioannidis","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Santa Clara, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3945-3640","authenticated-orcid":false,"given":"Soji","family":"Adeshina","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Santa Clara, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0074-6761","authenticated-orcid":false,"given":"Jiani","family":"Zhang","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Santa Clara, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3603-3341","authenticated-orcid":false,"given":"Xiao","family":"Qin","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Santa Clara, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2996-9790","authenticated-orcid":false,"given":"Christos","family":"Faloutsos","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Santa Clara, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8115-5415","authenticated-orcid":false,"given":"Da","family":"Zheng","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Santa Clara, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2753-1437","authenticated-orcid":false,"given":"George","family":"Karypis","sequence":"additional","affiliation":[{"name":"Amazon Web Services, Santa Clara, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3491-5968","authenticated-orcid":false,"given":"Philip S.","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Illinois at Chicago, Chicago, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Rie Kubota Ando and Tong Zhang. 2006. Learning on Graph with Laplacian Regularization. In NIPS. 25--32.","DOI":"10.7551\/mitpress\/7503.003.0009"},{"key":"e_1_3_2_1_2_1","unstructured":"Sanjeev Arora Nadav Cohen Wei Hu and Yuping Luo. 2019. Implicit Regularization in Deep Matrix Factorization. In NeurIPS. 7411--7422."},{"key":"e_1_3_2_1_3_1","volume-title":"International Conference on Machine Learning. PMLR, 1725--1735","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and deep graph convolutional networks. In International Conference on Machine Learning. PMLR, 1725--1735."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01612"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In The world wide web conference. 417--426.","DOI":"10.1145\/3308558.3313488"},{"key":"e_1_3_2_1_6_1","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. In NeurIPS."},{"key":"e_1_3_2_1_7_1","unstructured":"Johannes Gasteiger Stefan Wei\u00dfenberger and Stephan G\u00fcnnemann. 2019. Diffusion improves graph learning. In NeurIPS. 13366--13378."},{"key":"e_1_3_2_1_8_1","unstructured":"William L. Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS. 1024--1034."},{"key":"e_1_3_2_1_9_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"8634","author":"He Bobby","year":"2022","unstructured":"Bobby He and Mete Ozay. 2022. Exploring the Gap between Collapsed & Whitened Features in Self-Supervised Learning. In ICML (Proceedings of Machine Learning Research, Vol. 162). 8613--8634."},{"key":"e_1_3_2_1_10_1","unstructured":"Yang Hu Haoxuan You Zhecan Wang Zhicheng Wang Erjin Zhou and Yue Gao. 2021. Graph-MLP: Node Classification without Message Passing in Graph. https:\/\/arxiv.org\/abs\/2106.04051"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Tianyu Hua WenxiaoWang Zihui Xue Sucheng Ren YueWang and Hang Zhao. 2021. On Feature Decorrelation in Self-Supervised Learning. In ICCV. 9578--9588.","DOI":"10.1109\/ICCV48922.2021.00946"},{"key":"e_1_3_2_1_12_1","unstructured":"Ziwei Ji and Matus Telgarsky. 2019. Gradient descent aligns the layers of deep linear networks. In ICLR."},{"key":"e_1_3_2_1_13_1","unstructured":"Li Jing Pascal Vincent Yann LeCun and Yuandong Tian. 2022. Understanding Dimensional Collapse in Contrastive Self-supervised Learning. In ICLR."},{"key":"e_1_3_2_1_14_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2015","unstructured":"Diederik P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR."},{"key":"e_1_3_2_1_15_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_1_16_1","unstructured":"Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In ICLR."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Kanglin Liu Guoping Qiu Wenming Tang and Fei Zhou. 2019. Spectral Regularization for Combating Mode Collapse in GANs. In ICCV. 6381--6389.","DOI":"10.1109\/ICCV.2019.00648"},{"key":"e_1_3_2_1_18_1","unstructured":"Kenta Oono and Taiji Suzuki. 2019. Graph neural networks exponentially lose expressive power for node classification."},{"key":"e_1_3_2_1_19_1","unstructured":"Haozhi Qi Chong You Xiaolong Wang Yi Ma and Jitendra Malik. 2020. Deep isometric learning for visual recognition. In ICML. PMLR 7824--7835."},{"key":"e_1_3_2_1_20_1","volume-title":"International Conference on Machine Learning. PMLR","author":"St\u00e4rk Hannes","year":"2022","unstructured":"Hannes St\u00e4rk, Dominique Beaini, Gabriele Corso, Prudencio Tossou, Christian Dallago, Stephan G\u00fcnnemann, and Pietro Li\u00f2. 2022. 3d infomax improves gnns for molecular property prediction. In International Conference on Machine Learning. PMLR, 20479--20502."},{"key":"e_1_3_2_1_21_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Tian Yijun","year":"2022","unstructured":"Yijun Tian, Chuxu Zhang, Zhichun Guo, Xiangliang Zhang, and Nitesh Chawla. 2022. Learning mlps on graphs: A unified view of effectiveness, robustness, and efficiency. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_1_22_1","unstructured":"Rianne van den Berg Thomas N. Kipf and Max Welling. 2017. Graph Convolutional Matrix Completion."},{"key":"e_1_3_2_1_23_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Li\u00f2 and Yoshua Bengio. 2018. Graph Attention Networks. In ICLR."},{"key":"e_1_3_2_1_24_1","unstructured":"Minjie Wang Lingfan Yu Da Zheng Quan Gan Yu Gai Zihao Ye Mufei Li Jinjing Zhou Qi Huang Chao Ma Ziyue Huang Qipeng Guo Hao Zhang Haibin Lin Junbo Zhao Jinyang Li Alexander J. Smola and Zheng Zhang. 2019. Deep Graph Library: Towards Efficient and Scalable Deep Learning on Graphs."},{"key":"e_1_3_2_1_25_1","volume-title":"Molclr: Molecular contrastive learning of representations via graph neural networks.","author":"Wang Yuyang","year":"2021","unstructured":"Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani. 2021. Molclr: Molecular contrastive learning of representations via graph neural networks."},{"key":"e_1_3_2_1_26_1","volume-title":"Weinberger","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q.Weinberger. 2019. Simplifying Graph Convolutional Networks. In ICML (Proceedings of Machine Learning Research, Vol. 97). 6861--6871."},{"key":"e_1_3_2_1_27_1","unstructured":"Qitian Wu Hengrui Zhang Xiaofeng Gao Peng He Paul Weng Han Gao and Guihai Chen. 2019. Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems. In WWW. 2091--2102."},{"key":"e_1_3_2_1_28_1","unstructured":"Lechao Xiao Yasaman Bahri Jascha Sohl-Dickstein Samuel Schoenholz and Jeffrey Pennington. 2018. Dynamical isometry and a mean field theory of cnns: How to train 10 000-layer vanilla convolutional neural networks. In ICML. PMLR 5393--5402."},{"key":"e_1_3_2_1_29_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Han Yang Kaili Ma and James Cheng. 2021. Rethinking Graph Regularization for Graph Neural Networks. In AAAI. 4573--4581.","DOI":"10.1609\/aaai.v35i5.16586"},{"key":"e_1_3_2_1_31_1","volume-title":"ICML (Proceedings of Machine Learning Research","volume":"25279","author":"Yang Mingqi","year":"2022","unstructured":"Mingqi Yang, Yanming Shen, Rui Li, Heng Qi, Qiang Zhang, and Baocai Yin. 2022. A New Perspective on the Effects of Spectrum in Graph Neural Networks. In ICML (Proceedings of Machine Learning Research, Vol. 162). 25261--25279."},{"key":"e_1_3_2_1_32_1","unstructured":"Shichang Zhang Yozen Liu Yizhou Sun and Neil Shah. 2021. Graph-less Neural Networks: Teaching Old MLPs New Tricks via Distillation. In ICLR."},{"key":"e_1_3_2_1_33_1","volume-title":"Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods. In ICLR.","author":"Zheng Wenqing","year":"2021","unstructured":"Wenqing Zheng, Edward W. Huang, Nikhil Rao, Sumeet Katariya, Zhangyang Wang, and Karthik Subbian. 2021. Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoods. In ICLR."},{"key":"e_1_3_2_1_34_1","volume-title":"Jason Weston, and Bernhard Sch\u00f6lkopf.","author":"Zhou Dengyong","year":"2003","unstructured":"Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal, Jason Weston, and Bernhard Sch\u00f6lkopf. 2003. Learning with Local and Global Consistency. In NIPS. 321--328."}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Boise ID USA","acronym":"CIKM '24"},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679811","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679811","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:58:07Z","timestamp":1750294687000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679811"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":34,"alternative-id":["10.1145\/3627673.3679811","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679811","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}