{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,9]],"date-time":"2025-11-09T20:05:48Z","timestamp":1762718748815,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":25,"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":[{"name":"The Innovative Research Projects for Graduate Students of Heilongjiang University","award":["YJSCX2022-234HLJU"],"award-info":[{"award-number":["YJSCX2022-234HLJU"]}]},{"name":"The Natural Science Foundation of Heilongjiang Province in China","award":["No. LH2020F043"],"award-info":[{"award-number":["No. LH2020F043"]}]},{"name":"The National Natural Science Foundation of China","award":["No. 61972135"],"award-info":[{"award-number":["No. 61972135"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,10,17]]},"DOI":"10.1145\/3511808.3557606","type":"proceedings-article","created":{"date-parts":[[2022,10,16]],"date-time":"2022-10-16T01:29:57Z","timestamp":1665883797000},"page":"4682-4686","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Graph Representation Learning via Adaptive Multi-layer Neighborhood Diffusion Contrast"],"prefix":"10.1145","author":[{"given":"Jijie","family":"Zhang","sequence":"first","affiliation":[{"name":"Heilongjiang University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Yang","sequence":"additional","affiliation":[{"name":"Heilongjiang University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Heilongjiang University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Han","sequence":"additional","affiliation":[{"name":"Zhejiang University&Binjiang Institute of Zhejiang University, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaowei","family":"Yin","sequence":"additional","affiliation":[{"name":"Heilongjiang University, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2022,10,17]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"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 Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 119). 1725-- 1735 . Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119). 1725--1735."},{"key":"e_1_3_2_2_2_1","unstructured":"Weilin Cong Morteza Ramezani and Mehrdad Mahdavi. 2021. On Provable Benefits of Depth in Training Graph Convolutional Networks. In Advances in Neural Information Processing Systems 34. 9936--9949.  Weilin Cong Morteza Ramezani and Mehrdad Mahdavi. 2021. On Provable Benefits of Depth in Training Graph Convolutional Networks. In Advances in Neural Information Processing Systems 34. 9936--9949."},{"key":"e_1_3_2_2_3_1","volume-title":"Robustness, Fairness, and Explainability. arXiv preprint","author":"Dai Enyan","year":"2022","unstructured":"Enyan Dai , Tianxiang Zhao , Huaisheng Zhu , Junjie Xu , Zhimeng Guo , Hui Liu , Jiliang Tang , and Suhang Wang . 2022. A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy , Robustness, Fairness, and Explainability. arXiv preprint , Vol. arXiv: 2204 .08570 ( 2022 ). Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. 2022. A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability. arXiv preprint, Vol. arXiv:2204.08570 (2022)."},{"key":"e_1_3_2_2_4_1","volume-title":"GRAND: Scalable Graph Random Neural Networks. In WWW '22: The ACM Web Conference","author":"Feng Wenzheng","year":"2022","unstructured":"Wenzheng Feng , Yuxiao Dong , Tinglin Huang , Ziqi Yin , Xu Cheng , Evgeny Kharlamov , and Jie Tang . 2022 . GRAND: Scalable Graph Random Neural Networks. In WWW '22: The ACM Web Conference 2022. 3248--3258. Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, and Jie Tang. 2022. GRAND: Scalable Graph Random Neural Networks. In WWW '22: The ACM Web Conference 2022. 3248--3258."},{"key":"e_1_3_2_2_5_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 Advances in Neural Information Processing Systems 33.  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 Advances in Neural Information Processing Systems 33."},{"key":"e_1_3_2_2_6_1","volume-title":"Neural Message Passing for Quantum Chemistry. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"1272","author":"Gilmer Justin","unstructured":"Justin Gilmer , Samuel S. Schoenholz , Patrick F. Riley , Oriol Vinyals , and George E. Dahl . 2017 . Neural Message Passing for Quantum Chemistry. In Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 70). 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 Proceedings of the 34th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 70). PMLR, 1263--1272."},{"key":"e_1_3_2_2_7_1","volume-title":"Graph Structure Learning for Robust Graph Neural Networks. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 66--74","author":"Jin Wei","year":"2020","unstructured":"Wei Jin , Yao Ma , Xiaorui Liu , Xianfeng Tang , Suhang Wang , and Jiliang Tang . 2020 . Graph Structure Learning for Robust Graph Neural Networks. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 66--74 . Wei Jin, Yao Ma, Xiaorui Liu, Xianfeng Tang, Suhang Wang, and Jiliang Tang. 2020. Graph Structure Learning for Robust Graph Neural Networks. In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 66--74."},{"volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations. OpenReview.net.","author":"Thomas","key":"e_1_3_2_2_8_1","unstructured":"Thomas N. Kipf and Max Welling. 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations. OpenReview.net. Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_2_9_1","volume-title":"7th International Conference on Learning Representations. OpenReview.net.","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2019 . Predict then Propagate: Graph Neural Networks meet Personalized PageRank . In 7th International Conference on Learning Representations. OpenReview.net. Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In 7th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_2_11_1","volume-title":"DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses. arXiv preprint","author":"Li Yaxin","year":"2020","unstructured":"Yaxin Li , Wei Jin , Han Xu , and Jiliang Tang . 2020. DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses. arXiv preprint , Vol. arXiv: 2005 .06149 ( 2020 ). Yaxin Li, Wei Jin, Han Xu, and Jiliang Tang. 2020. DeepRobust: A PyTorch Library for Adversarial Attacks and Defenses. arXiv preprint, Vol. arXiv:2005.06149 (2020)."},{"key":"e_1_3_2_2_12_1","volume-title":"Towards Deeper Graph Neural Networks. In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 338--348","author":"Liu Meng","year":"2020","unstructured":"Meng Liu , Hongyang Gao , and Shuiwang Ji . 2020 . Towards Deeper Graph Neural Networks. In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 338--348 . Meng Liu, Hongyang Gao, and Shuiwang Ji. 2020. Towards Deeper Graph Neural Networks. In The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 338--348."},{"volume-title":"Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 43--52","author":"McAuley Julian J.","key":"e_1_3_2_2_13_1","unstructured":"Julian J. McAuley , Christopher Targett , Qinfeng Shi , and Anton van den Hengel. 2015. Image-Based Recommendations on Styles and Substitutes . In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 43--52 . Julian J. McAuley, Christopher Targett, Qinfeng Shi, and Anton van den Hengel. 2015. Image-Based Recommendations on Styles and Substitutes. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, 43--52."},{"key":"e_1_3_2_2_14_1","volume-title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In 8th International Conference on Learning Representations. OpenReview.net.","author":"Rong Yu","year":"2020","unstructured":"Yu Rong , Wenbing Huang , Tingyang Xu , and Junzhou Huang . 2020 . DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In 8th International Conference on Learning Representations. OpenReview.net. Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In 8th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_2_15_1","volume-title":"Graph Attention Networks. In 6th International Conference on Learning Representations. OpenReview.net.","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018 . Graph Attention Networks. In 6th International Conference on Learning Representations. OpenReview.net. Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_2_16_1","volume-title":"Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"6871","author":"Wu Felix","unstructured":"Felix Wu , Amauri H. Souza Jr ., Tianyi Zhang , Christopher Fifty , Tao Yu , and Kilian Q. Weinberger . 2019a . Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 97). PMLR, 6861-- 6871 . Felix Wu, Amauri H. Souza Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019a. Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 97). PMLR, 6861--6871."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2019\/669"},{"key":"e_1_3_2_2_18_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"5458","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Chengtao Li , Yonglong Tian , Tomohiro Sonobe , Ken-ichi Kawarabayashi, and Stefanie Jegelka . 2018 . Representation Learning on Graphs with Jumping Knowledge Networks . In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research , Vol. 80). PMLR, 5449-- 5458 . Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation Learning on Graphs with Jumping Knowledge Networks. In Proceedings of the 35th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 80). PMLR, 5449--5458."},{"key":"e_1_3_2_2_19_1","unstructured":"Wentao Zhang Mingyu Yang Zeang Sheng Yang Li Wen Ouyang Yangyu Tao Zhi Yang and Bin Cui. 2021. Node Dependent Local Smoothing for Scalable Graph Learning. In Advances in Neural Information Processing Systems 34. 20321--20332.  Wentao Zhang Mingyu Yang Zeang Sheng Yang Li Wen Ouyang Yangyu Tao Zhi Yang and Bin Cui. 2021. Node Dependent Local Smoothing for Scalable Graph Learning. In Advances in Neural Information Processing Systems 34. 20321--20332."},{"key":"e_1_3_2_2_20_1","volume-title":"Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. 2728--2737","author":"Zhou Kuangqi","year":"2021","unstructured":"Kuangqi Zhou , Yanfei Dong , Kaixin Wang , Wee Sun Lee , Bryan Hooi , Huan Xu , and Jiashi Feng . 2021 a. Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. 2728--2737 . Kuangqi Zhou, Yanfei Dong, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, and Jiashi Feng. 2021a. Understanding and Resolving Performance Degradation in Deep Graph Convolutional Networks. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management. 2728--2737."},{"key":"e_1_3_2_2_21_1","unstructured":"Kaixiong Zhou Xiao Huang Daochen Zha Rui Chen Li Li Soo-Hyun Choi and Xia Hu. 2021b. Dirichlet Energy Constrained Learning for Deep Graph Neural Networks. In Advances in Neural Information Processing Systems 34. 21834--21846.  Kaixiong Zhou Xiao Huang Daochen Zha Rui Chen Li Li Soo-Hyun Choi and Xia Hu. 2021b. Dirichlet Energy Constrained Learning for Deep Graph Neural Networks. In Advances in Neural Information Processing Systems 34. 21834--21846."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330851"},{"key":"e_1_3_2_2_23_1","volume-title":"Simple Spectral Graph Convolution. In 9th International Conference on Learning Representations. OpenReview.net.","author":"Zhu Hao","year":"2021","unstructured":"Hao Zhu and Piotr Koniusz . 2021 . Simple Spectral Graph Convolution. In 9th International Conference on Learning Representations. OpenReview.net. Hao Zhu and Piotr Koniusz. 2021. Simple Spectral Graph Convolution. In 9th International Conference on Learning Representations. OpenReview.net."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3220078"},{"key":"e_1_3_2_2_25_1","volume-title":"7th International Conference on Learning Representations. OpenReview.net.","author":"Z\u00fcgner Daniel","year":"2019","unstructured":"Daniel Z\u00fcgner and Stephan G\u00fcnnemann . 2019 . Adversarial Attacks on Graph Neural Networks via Meta Learning . In 7th International Conference on Learning Representations. OpenReview.net. Daniel Z\u00fcgner and Stephan G\u00fcnnemann. 2019. Adversarial Attacks on Graph Neural Networks via Meta Learning. In 7th International Conference on Learning Representations. OpenReview.net."}],"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\/3511808.3557606","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3511808.3557606","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:09Z","timestamp":1750182669000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3511808.3557606"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,17]]},"references-count":25,"alternative-id":["10.1145\/3511808.3557606","10.1145\/3511808"],"URL":"https:\/\/doi.org\/10.1145\/3511808.3557606","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"}}]}}