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ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, Yuxiao Dong. arXiv:2304.05977, 2023.
AgentBench: Evaluating LLMs as Agents. Xiao Liu, Hao Yu, Hanchen Zhang, Yifan Xu, Xuanyu Lei, Hanyu Lai, Yu Gu, Hangliang Ding, Kaiwen Men, Kejuan Yang, Shudan Zhang, Xiang Deng, Aohan Zeng, Zhengxiao Du, Chenhui Zhang, Sheng Shen, Tianjun Zhang, Yu Su, Huan Sun, Minlie Huang, Yuxiao Dong, Jie Tang. arXiv:2308.03688, 2023.
xTrimoPGLM: Unified 100B-Scale Pre-trained Transformer for Deciphering the Language of Protein. Bo Chen, Xingyi Cheng, Yangli-ao Geng, Shen Li, Xin Zeng, Boyan Wang, Jing Gong, Chiming Liu, Aohan Zeng, Yuxiao Dong, Jie Tang, Le Song. bioRxiv, 2023.
WebGLM: Towards An Efficient Web-enhanced Question Answering System with Human Preference. Xiao Liu, Hanyu Lai, Yu Hao, Yifan Xu, Aohan Zeng, Zhengxiao Du, Peng Zhang, Yuxiao Dong, Jie Tang. KDD'23 (Proc. of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2023.
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X. Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang. KDD'23 (Proc. of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2023.
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs. Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Zhitao Ying, Yukuo Cen, Yangliao Geng, Jie Tang. KDD'23 (Proc. of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2023.
WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window. Yifan Zhu, Cong Fangpeng, Dan Zhang, Wenwen Gong, Qika Lin, wenzheng feng, Yuxiao Dong, Jie Tang. KDD'23 (Proc. of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2023.
Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit. Bo Chen, Jing Zhang, Fanjin Zhang, Tianyi Han, Yuqing Cheng, XiaoYan Li, Yuxiao Dong, Jie Tang. KDD'23 (Proc. of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2023.
GLM-130B: An Open Bilingual Pre-trained Model. Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, Weng Lam Tam, Zixuan Ma, Yufei Xue, Jidong Zhai, Wenguang Chen, Zhiyuan Liu, Peng Zhang, Yuxiao Dong, Jie Tang. ICLR'23 ( In Proceedings of the 11th International Conference on Learning Representations), 2023.
SketchNE: Embedding Billion-Scale Networks Accurately in One Hour. Yuyang Xie, Yuxiao Dong, Jiezhong Qiu, Wenjian Yu, Xu Feng, Jie Tang. TKDE'23 (IEEE Transaction on Knowledge and Data Engineering), 2023.
GraphMAE2: A Decoding-enhanced Masked Self-supervised Graph Learner. Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang. WWW'23 (Proceedings of The Web Conference 2023), 2023.
GraphMAE: Self-Supervised Masked Graph Autoencoders. Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang. KDD'22 (Proc. of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2022.
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries. Xiao Liu, Shiyu Zhao, Kai Su, Yukuo Cen, Jiezhong Qiu, Mengdi Zhang, Wei Wu, Yuxiao Dong, Jie Tang. KDD'22 (Proc. of the 28th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2022.
Science as a Public Good: Public Use and Funding of Science. Yian Yin, Yuxiao Dong, Kuansan Wang, Dashun Wang, Benjamin Jones. Nature Human Behavior, 2022. (accepted)
EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs. Namyong Park, Fuchen Liu, Purvanshi Mehta, Elena Cristofor, Christos Faloutsos, Yuxiao Dong. WSDM'22 (Proc. of the 15th ACM International Conference on Web Search and Data Mining), 2022.
GRAND+: Scalable Graph Random Neural Networks. Wenzheng Feng, Yuxiao Dong, Huang Tinglin, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang. WWW'22 (Proceedings of The Web Conference 2022), 2022.
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs. Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang. WWW'22 (Proceedings of The Web Conference 2022), 2022.
ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs. Yanling Wang, Jing Zhang, Haoyang Li, Yuxiao Dong, Hongzhi Yin, Cuiping Li, Hong Chen. WWW'22 (Proceedings of The Web Conference 2022), 2022.
Adaptive Diffusion in Graph Neural Networks. Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang. NeurIPS'21 (Proc. of the 35th Annual Conference on Neural Information Processing Systems), 2021.
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs. Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec. NeurIPS'21 D&B (Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks), 2021.
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang. NeurIPS'21 D&B (Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks), 2021.
A Large-Scale Database for Graph Representation Learning. Scott Freitas, Yuxiao Dong, Joshua Neil, Duen Horng Chau. NeurIPS'21 D&B (Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks), 2021.
MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems Tinglin Huang, Yuxiao Dong, Ming Ding, Zhen Yang, Wenzheng Feng, Xinyu Wang, Jie Tang. KDD'21 (Proc. of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2021.
TDGIA: Effective Injection Attacks on Graph Neural Networks Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang. KDD'21 (Proc. of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2021. Full Research Paper.
Are We Really Making Much Progress? Revisiting, Benchmarking and Refining the Heterogeneous Graph Neural Networks Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang. KDD'21 (Proc. of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2021.
MATCH: Metadata-Aware Text Classification in A Large Hierarchy Yu Zhang, Zhihong Shen, Yuxiao Dong, Kuansan Wang, Jiawei Han. WWW'21 (Proceedings of The Web Conference 2021), 2021.
GPT-GNN: Generative Pre-Training of Graph Neural Networks Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun KDD'20 (Proc. of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2020.
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang KDD'20 (Proc. of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2020.
Open Graph Benchmark: Datasets for Machine Learning on Graphs Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec NeurIPS'20 (Proc. of the 34th Annual Conference on Neural Information Processing Systems), 2020.
Graph Random Neural Networks for Semi-Supervised Learning on Graphs Wenzheng Feng=, Jie Zhang=, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang NeurIPS'20 (Proc. of the 34th Annual Conference on Neural Information Processing Systems), 2020.
Heterogeneous Graph Transformer Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun. WWW'20 (Proc. of the 2020 Web Conference), short paper, oral.
Heterogeneous Network Representation Learning Yuxiao Dong, Ziniu Hu, Kuansan Wang, Yizhou Sun, Jie Tang. IJCAI'20 (Proc. of the 29th International Joint Conference on Artificial Intelligence), 2020.
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang and Jie Tang WWW'19 (Proc. of the 2019 Web Conference), 2019.
ProNE: Fast and Scalable Network Representation Learning Jie Zhang, Yuxiao Dong, Yan Wang, Jie Tang, Ming Ding. IJCAI'19 (Proc. of the 28th International Joint Conference on Artificial Intelligence), 2019.
OAG: Toward Linking Large-scale Heterogeneous Entity Graphs Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, Kuansan Wang KDD'19 (Proc. of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2019.
Neural Tensor Factorization Xian Wu, Baoxu Shi,Yuxiao Dong, Chao Huang, Nitesh V. Chawla. WSDM'19 (Proc. of the 12th ACM International Conference on Web Search and Data Mining), 2019.
Mining Fraudsters and Fraudulent Strategies in Large-Scale Mobile Social Networks Yang Yang, Yuhong Xu, Yizhou Sun, Yuxiao Dong, Fei Wu, Yueting Zhuang. TKDE'19 (IEEE Transaction on Knowledge and Data Engineering), 2019.
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang. WSDM'18 (Proc. of the 11th ACM International Conference on Web Search and Data Mining), 2018.
DeepInf: Social Influence Prediction with Deep Learning Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang. KDD'18 (Proc. of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), Full paper (poster presentation), 2018.
Will Triadic Closure Strengthen Ties in Social Networks? Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, Xiaoming Fu. TKDD 2018 (ACM Transactions on Knowledge Discovery from Data), 2018.
Computational Lens on Big Social and Information Networks. Ph.D. dissertation, University of Notre Dame, 2017.
metapath2vec: Scalable Representation Learning for Heterogeneous Networks. Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami. KDD'17 (Proc. of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2017.
Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks. Yuxiao Dong, Reid A. Johnson, Jian Xu, Nitesh V. Chawla. KDD'17 (Proc. of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2017.
A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations. Yuxiao Dong, Hao Ma, Zhihong Shen, Kuansan Wang. KDD'17 (Proc. of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2017.
User Modeling on Demographic Attributes in Big Mobile Social Networks. Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, Yang Yang. TOIS 2017 (ACM Transactions on Information Systems), 2017.
Can Scientific Impact Be Predicted? Yuxiao Dong=, Reid A. Johnson=, Nitesh V. Chawla. (=Equal Contribution) TBD 2016 (IEEE Transactions on Big Data), 2016.
Will This Paper Increase Your h-index? Scientific Impact Prediction. Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla. WSDM'15 (Proc. of the 8th ACM International Conference on Web Search and Data Mining), 2015.
CoupledLP: Link Prediction in Coupled Networks. Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V. Chawla, Bai Wang. KDD'15 (Proc. of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2015.
Inferring User Demographics and Social Strategies in Mobile Social Networks. Yuxiao Dong, Yang Yang, Jie Tang, Yang Yang, Nitesh V. Chawla. KDD'14 (Proc. of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining), 2014.
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