Carl J. Yang

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Jiawei HanAbel Bliss Professor of Computer Science, University of IllinoisDirección de correo verificada de cs.uiuc.edu
Hejie CuiEmory UniversityDirección de correo verificada de emory.edu
Lichao "James" SunAssistant Professor of Computer Science, Lehigh UniversityDirección de correo verificada de lehigh.edu
Xuan KanEmory UniversityDirección de correo verificada de emory.edu
Lifang HeAssistant Professor of Computer Science, Lehigh UniversityDirección de correo verificada de lehigh.edu
Chao ZhangAssistant Professor at College of Computing, Georgia TechDirección de correo verificada de gatech.edu
Yanqiao ZHUUniversity of California, Los AngelesDirección de correo verificada de cs.ucla.edu
Jiaying LuEmory UniversityDirección de correo verificada de emory.edu
Philip S. YuProfessor of Computer Science, University of Illinons at ChicagoDirección de correo verificada de cs.uic.edu
Pan LiGeorgia Institute of Technology, Purdue UniversityDirección de correo verificada de gatech.edu
Yuxin XiaoMassachusetts Institute of TechnologyDirección de correo verificada de mit.edu
Jieyu ZhangUniversity of WashingtonDirección de correo verificada de cs.washington.edu
Ke ZhangClusterTech LimitedDirección de correo verificada de connect.hku.hk
Liang ChenAssociate Professor, Sun Yat-Sen UniversityDirección de correo verificada de mail.sysu.edu.cn
Yu ZhangUniversity of Illinois at Urbana-ChampaignDirección de correo verificada de illinois.edu
Luo JieNianticDirección de correo verificada de nianticlabs.com
Quan YuanMetaDirección de correo verificada de meta.com
Qi ZhuAmazon Web ServicesDirección de correo verificada de illinois.edu
Yu ShiResearch Scientist at FacebookDirección de correo verificada de fb.com
Chaoyang HeCo-founder, FedML Inc; PhD in CS from University of Southern CaliforniaDirección de correo verificada de usc.edu

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Carl J. Yang

Assistant Professor of Computer Science at Emory University

Dirección de correo verificada de emory.edu - Página principal

graph data mining applied machine learning knowledge graphs federated learning


Título Ordenar por citas Ordenar por año Ordenar por título	Citado por Citado por	Año
Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation C Yang, L Bai, C Zhang, Q Yuan, J Han Proceedings of the 23rd ACM SIGKDD international conference on knowledge …, 2017	355	2017
Heterogeneous network representation learning: A unified framework with survey and benchmark C Yang, Y Xiao, Y Zhang, Y Sun, J Han IEEE Transactions on Knowledge and Data Engineering 34 (10), 4854-4873, 2020	288*	2020
Adversarial attack and defense on graph data: A survey L Sun, Y Dou, C Yang, J Wang, PS Yu, B Li arXiv preprint arXiv:1812.10528, 2018	273*	2018
Fedgraphnn: A federated learning system and benchmark for graph neural networks C He, K Balasubramanian, E Ceyani, C Yang, H Xie, L Sun, L He, L Yang, ... arXiv preprint arXiv:2104.07145, 2021	142	2021
Federated graph classification over non-iid graphs H Xie, J Ma, L Xiong, C Yang Advances in neural information processing systems 34, 18839-18852, 2021	103	2021
Subgraph federated learning with missing neighbor generation K Zhang, C Yang, X Li, L Sun, SM Yiu Advances in Neural Information Processing Systems 34, 6671-6682, 2021	103	2021
I know you'll be back: Interpretable new user clustering and churn prediction on a mobile social application C Yang, X Shi, L Jie, J Han Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018	102	2018
Conditional structure generation through graph variational generative adversarial nets C Yang, P Zhuang, W Shi, A Luu, P Li Advances in neural information processing systems 32, 2019	98	2019
Transfer learning of graph neural networks with ego-graph information maximization Q Zhu, C Yang, Y Xu, H Wang, C Zhang, J Han Advances in Neural Information Processing Systems 34, 1766-1779, 2021	86	2021
When do gnns work: Understanding and improving neighborhood aggregation Y Xie, S Li, C Yang, RCW Wong, J Han IJCAI'20: Proceedings of the Twenty-Ninth International Joint Conference on …, 2020	66	2020
mvn2vec: Preservation and collaboration in multi-view network embedding Y Shi, F Han, X He, X He, C Yang, J Luo, J Han arXiv preprint arXiv:1801.06597, 2018	65	2018
Braingb: a benchmark for brain network analysis with graph neural networks H Cui, W Dai, Y Zhu, X Kan, AAC Gu, J Lukemire, L Zhan, L He, Y Guo, ... IEEE transactions on medical imaging 42 (2), 493-506, 2022	62	2022
On positional and structural node features for graph neural networks on non-attributed graphs H Cui, Z Lu, P Li, C Yang Proceedings of the 31st ACM International Conference on Information …, 2022	62	2022
Brain network transformer X Kan, W Dai, H Cui, Z Zhang, Y Guo, C Yang Advances in Neural Information Processing Systems 35, 25586-25599, 2022	60	2022
Fbnetgen: Task-aware gnn-based fmri analysis via functional brain network generation X Kan, H Cui, J Lukemire, Y Guo, C Yang International Conference on Medical Imaging with Deep Learning, 618-637, 2022	59	2022
Interpretable graph neural networks for connectome-based brain disorder analysis H Cui, W Dai, Y Zhu, X Li, L He, C Yang International Conference on Medical Image Computing and Computer-Assisted …, 2022	55	2022
Understanding structural vulnerability in graph convolutional networks L Chen, J Li, Q Peng, Y Liu, Z Zheng, C Yang arXiv preprint arXiv:2108.06280, 2021	51	2021
Local coordinate concept factorization for image representation H Liu, Z Yang, C Yang, Z Wu, X Li IEEE Transactions on Neural Networks and Learning Systems 25 (6), 1071-1082, 2013	50	2013
MultiSage: Empowering GCN with contextualized multi-embeddings on web-scale multipartite networks C Yang, A Pal, A Zhai, N Pancha, J Han, C Rosenberg, J Leskovec Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020	49	2020
Relation learning on social networks with multi-modal graph edge variational autoencoders C Yang, J Zhang, H Wang, S Li, M Kim, M Walker, Y Xiao, J Han Proceedings of the 13th International Conference on Web Search and Data …, 2020	48	2020

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