{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T17:13:47Z","timestamp":1780766027500,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":35,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"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":[[2022,4,25]]},"DOI":"10.1145\/3485447.3512100","type":"proceedings-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T05:11:23Z","timestamp":1650863483000},"page":"1270-1280","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":22,"title":["Meta-Weight Graph Neural Network: Push the Limits Beyond Global Homophily"],"prefix":"10.1145","author":[{"given":"Xiaojun","family":"Ma","sequence":"first","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qin","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyi","family":"Ren","sequence":"additional","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guojie","family":"Song","sequence":"additional","affiliation":[{"name":"Peking University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Wang","sequence":"additional","affiliation":[{"name":"Alibaba Inc, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg\u00a0Ver Steeg, and Aram Galstyan. 2019. MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA(Proceedings of Machine Learning Research, Vol.\u00a097), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 21\u201329."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.74.47"},{"key":"e_1_3_2_1_3_1","volume-title":"Proceedings of the 2nd International Conference on Learning Representations.","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann Lecun. 2014. Spectral networks and locally connected networks on graphs. In Proceedings of the 2nd International Conference on Learning Representations."},{"key":"e_1_3_2_1_4_1","unstructured":"Chen Cai and Yusu Wang. 2018. A simple yet effective baseline for non-attributed graph classification. arXiv preprint arXiv:1811.03508(2018)."},{"key":"e_1_3_2_1_5_1","unstructured":"Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In ICML(Proceedings of Machine Learning Research Vol.\u00a0119). PMLR 1725\u20131735."},{"key":"e_1_3_2_1_6_1","volume-title":"Karthik Subbian, and Lada\u00a0A. Adamic.","author":"Cheng Justin","year":"2018","unstructured":"Justin Cheng, Jon\u00a0M. Kleinberg, Jure Leskovec, David Liben-Nowell, Bogdan State, Karthik Subbian, and Lada\u00a0A. Adamic. 2018. Do Diffusion Protocols Govern Cascade Growth?. In ICWSM. AAAI Press, 32\u201341."},{"key":"e_1_3_2_1_7_1","volume-title":"Adaptive Universal Generalized PageRank Graph Neural Network. In 9th International Conference on Learning Representations, ICLR 2021","author":"Chien Eli","year":"2021","unstructured":"Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021. Adaptive Universal Generalized PageRank Graph Neural Network. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1"},{"key":"e_1_3_2_1_9_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in neural information processing systems. 3844\u20133852."},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/305219.305248"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289026"},{"key":"e_1_3_2_1_13_1","volume-title":"Kingma and Jimmy Ba","author":"P.","year":"2015","unstructured":"Diederik\u00a0P. Kingma and Jimmy Ba. 2015. Adam: A Method for Stochastic Optimization. In ICLR (Poster)."},{"key":"e_1_3_2_1_14_1","volume-title":"Proceedings of the 5th International Conference on Learning Representations.","author":"N.","unstructured":"Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations."},{"key":"e_1_3_2_1_15_1","unstructured":"Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In ICLR (Poster). OpenReview.net."},{"key":"e_1_3_2_1_16_1","volume-title":"DeepGCNs: Can GCNs Go As Deep As CNNs?","author":"Li Guohao","unstructured":"Guohao Li, Matthias M\u00fcller, Ali\u00a0K. Thabet, and Bernard Ghanem. 2019. DeepGCNs: Can GCNs Go As Deep As CNNs?. In ICCV. IEEE, 9266\u20139275."},{"key":"e_1_3_2_1_17_1","volume-title":"Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning","author":"Li Qimai","unstructured":"Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning. In AAAI, Sheila\u00a0A. McIlraith and Kilian\u00a0Q. Weinberger (Eds.). AAAI Press, 3538\u20133545."},{"key":"e_1_3_2_1_18_1","unstructured":"Renjie Liao Zhizhen Zhao Raquel Urtasun and Richard\u00a0S. Zemel. 2019. LanczosNet: Multi-Scale Deep Graph Convolutional Networks. In ICLR (Poster). OpenReview.net."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Meng Liu Hongyang Gao and Shuiwang Ji. 2020. Towards Deeper Graph Neural Networks. In KDD. ACM 338\u2013348.","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_3_2_1_20_1","volume-title":"Geom-GCN: Geometric Graph Convolutional Networks. In 8th International Conference on Learning Representations, ICLR 2020","author":"Pei Hongbin","year":"2020","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin\u00a0Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-GCN: Geometric Graph Convolutional Networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098061"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNN.2008.2005605"},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the 6th International Conference on Learning Representations.","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 Proceedings of the 6th International Conference on Learning Representations."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403177"},{"key":"e_1_3_2_1_26_1","first-page":"12","article-title":"A Reduction of a Graph to a Canonical Form and an Algebra Arising During This Reduction","volume":"2","author":"Weisfeiler Boris","year":"1968","unstructured":"Boris Weisfeiler and A.\u00a0A. Lehman. 1968. A Reduction of a Graph to a Canonical Form and an Algebra Arising During This Reduction. Nauchno-Technicheskaya Informatsia Ser. 2, N9 (1968), 12\u201316.","journal-title":"Nauchno-Technicheskaya Informatsia Ser."},{"key":"e_1_3_2_1_27_1","unstructured":"Felix Wu Amauri H.\u00a0Souza Jr. Tianyi Zhang Christopher Fifty Tao Yu and Kilian\u00a0Q. Weinberger. 2019. Simplifying Graph Convolutional Networks. In ICML(Proceedings of Machine Learning Research Vol.\u00a097). PMLR 6861\u20136871."},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016(JMLR Workshop and Conference Proceedings, Vol.\u00a048)","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang, William\u00a0W. Cohen, and Ruslan Salakhutdinov. 2016. Revisiting Semi-Supervised Learning with Graph Embeddings. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016(JMLR Workshop and Conference Proceedings, Vol.\u00a048), Maria-Florina Balcan and Kilian\u00a0Q. Weinberger (Eds.). JMLR.org, 40\u201348."},{"key":"e_1_3_2_1_29_1","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?CoRR abs\/2106.05234(2021). arXiv:2106.05234"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17283"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.5555\/3327345.3327537"},{"key":"e_1_3_2_1_32_1","unstructured":"Jiaxuan You Rex Ying and Jure Leskovec. 2019. Position-aware Graph Neural Networks. In ICML(Proceedings of Machine Learning Research Vol.\u00a097). PMLR 7134\u20137143."},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097)","author":"Yu Yue","year":"2019","unstructured":"Yue Yu, Jie Chen, Tian Gao, and Mo Yu. 2019. DAG-GNN: DAG Structure Learning with Graph Neural Networks. In Proceedings of the 36th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a097), Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.). PMLR, 7154\u20137163."},{"key":"e_1_3_2_1_34_1","volume-title":"Graph Neural Networks with Heterophily","author":"Zhu Jiong","unstructured":"Jiong Zhu, Ryan\u00a0A. Rossi, Anup Rao, Tung Mai, Nedim Lipka, Nesreen\u00a0K. Ahmed, and Danai Koutra. 2021. Graph Neural Networks with Heterophily. In AAAI. AAAI Press, 11168\u201311176."},{"key":"e_1_3_2_1_35_1","unstructured":"Jiong Zhu Yujun Yan Lingxiao Zhao Mark Heimann Leman Akoglu and Danai Koutra. 2020. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. In NeurIPS Hugo Larochelle Marc\u2019Aurelio Ranzato Raia Hadsell Maria-Florina Balcan and Hsuan-Tien Lin (Eds.)."}],"event":{"name":"WWW '22: The ACM Web Conference 2022","location":"Virtual Event, Lyon France","acronym":"WWW '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2022"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512100","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512100","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:30:08Z","timestamp":1750188608000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":35,"alternative-id":["10.1145\/3485447.3512100","10.1145\/3485447"],"URL":"https:\/\/doi.org\/10.1145\/3485447.3512100","relation":{},"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"2022-04-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}