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2020 – today
- 2023
[j17]Xiao Liu
, Li Mian, Yuxiao Dong, Fanjin Zhang, Jing Zhang, Jie Tang, Peng Zhang, Jibing Gong, Kuansan Wang:
OAG$_{\mathrm {know}}$ know : Self-Supervised Learning for Linking Knowledge Graphs. IEEE Trans. Knowl. Data Eng. 35(2): 1895-1908 (2023)- [j16]Zhenyu Hou, Yukuo Cen, Yuxiao Dong, Jie Zhang, Jie Tang:
Automated Unsupervised Graph Representation Learning. IEEE Trans. Knowl. Data Eng. 35(3): 2285-2298 (2023) - [j15]Bo Chen, Jing Zhang, Xiaokang Zhang, Yuxiao Dong, Jian Song, Peng Zhang, Kaibo Xu, Evgeny Kharlamov, Jie Tang:
GCCAD: Graph Contrastive Coding for Anomaly Detection. IEEE Trans. Knowl. Data Eng. 35(8): 8037-8051 (2023) - [j14]Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Evgeny Kharlamov, Bin Shao, Rui Li, Kuansan Wang:
OAG: Linking Entities Across Large-Scale Heterogeneous Knowledge Graphs. IEEE Trans. Knowl. Data Eng. 35(9): 9225-9239 (2023) - [j13]Yuyang Xie, Yuxiao Dong, Jiezhong Qiu, Wenjian Yu, Xu Feng, Jie Tang:
SketchNE: Embedding Billion-Scale Networks Accurately in One Hour. IEEE Trans. Knowl. Data Eng. 35(10): 10666-10680 (2023) - [c75]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:
GLM-130B: An Open Bilingual Pre-trained Model. ICLR 2023 - [c74]Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang:
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs. KDD 2023: 3057-3069 - [c73]Yifan Zhu, Fangpeng Cong, Dan Zhang, Wenwen Gong, Qika Lin, Wenzheng Feng, Yuxiao Dong, Jie Tang:
WinGNN: Dynamic Graph Neural Networks with Random Gradient Aggregation Window. KDD 2023: 3650-3662 - [c72]Bo Chen, Jing Zhang, Fanjin Zhang, Tianyi Han, Yuqing Cheng, Xiaoyan Li, Yuxiao Dong, Jie Tang:
Web-Scale Academic Name Disambiguation: The WhoIsWho Benchmark, Leaderboard, and Toolkit. KDD 2023: 3817-3828 - [c71]Xiao Liu, Hanyu Lai, Hao Yu, Yifan Xu, Aohan Zeng, Zhengxiao Du, Peng Zhang, Yuxiao Dong, Jie Tang:
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences. KDD 2023: 4549-4560 - [c70]Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Lei Shen, Zihan Wang, Andi Wang, Yang Li, Teng Su, Zhilin Yang, Jie Tang:
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X. KDD 2023: 5673-5684 - [c69]Jiaqi Ma, Jiong Zhu, Yuxiao Dong, Danai Koutra, Jingrui He, Qiaozhu Mei, Anton Tsitsulin, Xingjian Zhang, Marinka Zitnik:
The 3rd Workshop on Graph Learning Benchmarks (GLB 2023). KDD 2023: 5870-5871 - [c68]Ming Zhou, Wenzheng Feng, Yifan Zhu, Dan Zhang, Yuxiao Dong, Jie Tang:
Semi-Supervised Social Bot Detection with Initial Residual Relation Attention Networks. ECML/PKDD (6) 2023: 207-224 - [c67]Qing Li, Xiao Huang, Ninghao Liu, Yuxiao Dong, Guansong Pang:
International Workshop on Learning with Knowledge Graphs: Construction, Embedding, and Reasoning. WSDM 2023: 1273-1274 - [c66]Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang:
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner. WWW 2023: 737-746 - [c65]Yukuo Cen, Zhenyu Hou, Yan Wang, Qibin Chen, Yizhen Luo, Zhongming Yu, Hengrui Zhang, Xingcheng Yao, Aohan Zeng, Shiguang Guo, Yuxiao Dong, Yang Yang, Peng Zhang, Guohao Dai, Yu Wang, Chang Zhou, Hongxia Yang, Jie Tang:
CogDL: A Comprehensive Library for Graph Deep Learning. WWW 2023: 747-758 - [c64]Dan Zhang, Yifan Zhu, Yuxiao Dong, Yuandong Wang, Wenzheng Feng, Evgeny Kharlamov, Jie Tang:
ApeGNN: Node-Wise Adaptive Aggregation in GNNs for Recommendation. WWW 2023: 759-769 - [i50]Bo Chen, Jing Zhang, Fanjin Zhang, Tianyi Han, Yuqing Cheng, Xiaoyan Li, Yuxiao Dong, Jie Tang:
Web-Scale Academic Name Disambiguation: the WhoIsWho Benchmark, Leaderboard, and Toolkit. CoRR abs/2302.11848 (2023) - [i49]Ji Qi, Jifan Yu, Teng Tu, Kunyu Gao, Yifan Xu, Xinyu Guan, Xiaozhi Wang, Yuxiao Dong, Bin Xu, Lei Hou, Juanzi Li, Jie Tang, Weidong Guo, Hui Liu, Yu Xu:
GOAL: A Challenging Knowledge-grounded Video Captioning Benchmark for Real-time Soccer Commentary Generation. CoRR abs/2303.14655 (2023) - [i48]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:
CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Evaluations on HumanEval-X. CoRR abs/2303.17568 (2023) - [i47]Zhenyu Hou, Yufei He, Yukuo Cen, Xiao Liu, Yuxiao Dong, Evgeny Kharlamov, Jie Tang:
GraphMAE2: A Decoding-Enhanced Masked Self-Supervised Graph Learner. CoRR abs/2304.04779 (2023) - [i46]Jiazheng Xu, Xiao Liu, Yuchen Wu, Yuxuan Tong, Qinkai Li, Ming Ding, Jie Tang, Yuxiao Dong:
ImageReward: Learning and Evaluating Human Preferences for Text-to-Image Generation. CoRR abs/2304.05977 (2023) - [i45]Kejuan Yang, Xiao Liu, Kaiwen Men, Aohan Zeng, Yuxiao Dong, Jie Tang:
Revisiting Parallel Context Windows: A Frustratingly Simple Alternative and Chain-of-Thought Deterioration. CoRR abs/2305.15262 (2023) - [i44]Zhen Yang, Tinglin Huang, Ming Ding, Yuxiao Dong, Rex Ying, Yukuo Cen, Yangliao Geng, Jie Tang:
BatchSampler: Sampling Mini-Batches for Contrastive Learning in Vision, Language, and Graphs. CoRR abs/2306.03355 (2023) - [i43]Xiao Liu, Hanyu Lai, Hao Yu, Yifan Xu, Aohan Zeng, Zhengxiao Du, Peng Zhang, Yuxiao Dong, Jie Tang:
WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences. CoRR abs/2306.07906 (2023) - [i42]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:
AgentBench: Evaluating LLMs as Agents. CoRR abs/2308.03688 (2023) - [i41]Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li:
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding. CoRR abs/2308.14508 (2023) - 2022
- [j12]Qinkai Zheng, Xiao Xia, Kun Zhang, Evgeny Kharlamov, Yuxiao Dong:
On the distribution alignment of propagation in graph neural networks. AI Open 3: 218-228 (2022) - [j11]Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang, Xiao Liu, Ruobing Xie, Kai Zhuang, Xu Zhang, Leyu Lin, Philip S. Yu:
Understanding WeChat User Preferences and "Wow" Diffusion. IEEE Trans. Knowl. Data Eng. 34(12): 6033-6046 (2022) - [c63]Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang:
GraphMAE: Self-Supervised Masked Graph Autoencoders. KDD 2022: 594-604 - [c62]Xiao Liu, Shiyu Zhao, Kai Su, Yukuo Cen, Jiezhong Qiu, Mengdi Zhang, Wei Wu, Yuxiao Dong, Jie Tang:
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries. KDD 2022: 1120-1130 - [c61]Xiao Liu, Da Yin, Jingnan Zheng, Xingjian Zhang, Peng Zhang, Hongxia Yang, Yuxiao Dong, Jie Tang:
OAG-BERT: Towards a Unified Backbone Language Model for Academic Knowledge Services. KDD 2022: 3418-3428 - [c60]Zhuofeng Wu, Sinong Wang, Jiatao Gu, Rui Hou, Yuxiao Dong, V. G. Vinod Vydiswaran, Hao Ma:
IDPG: An Instance-Dependent Prompt Generation Method. NAACL-HLT 2022: 5507-5521 - [c59]Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, Yuxiao Dong:
EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs. WSDM 2022: 794-803 - [c58]Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang:
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs. WWW 2022: 860-870 - [c57]Yanling Wang, Jing Zhang, Haoyang Li, Yuxiao Dong, Hongzhi Yin, Cuiping Li, Hong Chen:
ClusterSCL: Cluster-Aware Supervised Contrastive Learning on Graphs. WWW 2022: 1611-1621 - [c56]Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang:
GRAND+: Scalable Graph Random Neural Networks. WWW 2022: 3248-3258 - [i40]Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, Yuxiao Dong:
EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs. CoRR abs/2202.07648 (2022) - [i39]Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang:
SelfKG: Self-Supervised Entity Alignment in Knowledge Graphs. CoRR abs/2203.01044 (2022) - [i38]Wenzheng Feng, Yuxiao Dong, Tinglin Huang, Ziqi Yin, Xu Cheng, Evgeny Kharlamov, Jie Tang:
GRAND+: Scalable Graph Random Neural Networks. CoRR abs/2203.06389 (2022) - [i37]Zhuofeng Wu, Sinong Wang, Jiatao Gu, Rui Hou, Yuxiao Dong, V. G. Vinod Vydiswaran, Hao Ma:
IDPG: An Instance-Dependent Prompt Generation Method. CoRR abs/2204.04497 (2022) - [i36]Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, Jie Tang:
GraphMAE: Self-Supervised Masked Graph Autoencoders. CoRR abs/2205.10803 (2022) - [i35]Weng Lam Tam, Xiao Liu, Kaixuan Ji, Lilong Xue, Xingjian Zhang, Yuxiao Dong, Jiahua Liu, Maodi Hu, Jie Tang:
Parameter-Efficient Prompt Tuning Makes Generalized and Calibrated Neural Text Retrievers. CoRR abs/2207.07087 (2022) - [i34]Xiao Liu, Shiyu Zhao, Kai Su, Yukuo Cen, Jiezhong Qiu, Mengdi Zhang, Wei Wu, Yuxiao Dong, Jie Tang:
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries. CoRR abs/2208.07638 (2022) - [i33]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, Peng Zhang, Yuxiao Dong, Jie Tang:
GLM-130B: An Open Bilingual Pre-trained Model. CoRR abs/2210.02414 (2022) - 2021
- [j10]Yuxiao Dong, Marinka Zitnik:
Guest Editorial: AI for COVID-19. IEEE Trans. Big Data 7(1): 1-2 (2021) - [j9]Yang Yang, Yuhong Xu, Yizhou Sun, Yuxiao Dong, Fei Wu, Yueting Zhuang:
Mining Fraudsters and Fraudulent Strategies in Large-Scale Mobile Social Networks. IEEE Trans. Knowl. Data Eng. 33(1): 169-179 (2021) - [c55]Jingwen Xu, Jing Zhang, Xirui Ke, Yuxiao Dong, Hong Chen, Cuiping Li, Yongbin Liu:
P-INT: A Path-based Interaction Model for Few-shot Knowledge Graph Completion. EMNLP (Findings) 2021: 385-394 - [c54]Tinglin Huang, Yuxiao Dong, Ming Ding, Zhen Yang, Wenzheng Feng, Xinyu Wang, Jie Tang:
MixGCF: An Improved Training Method for Graph Neural Network-based Recommender Systems. KDD 2021: 665-674 - [c53]Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang:
Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks. KDD 2021: 1150-1160 - [c52]Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang:
TDGIA: Effective Injection Attacks on Graph Neural Networks. KDD 2021: 2461-2471 - [c51]Ming Ding, Yuxiao Dong, Xiao Liu, Jiezhong Qiu, Jie Tang, Zhilin Yang:
The International Workshop on Pretraining: Algorithms, Architectures, and Applications ([email protected] 2021). KDD 2021: 4119-4120 - [c50]Scott Freitas, Yuxiao Dong, Joshua Neil, Duen Horng Chau:
A Large-Scale Database for Graph Representation Learning. NeurIPS Datasets and Benchmarks 2021 - [c49]Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec:
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs. NeurIPS Datasets and Benchmarks 2021 - [c48]Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, Jie Tang:
Adaptive Diffusion in Graph Neural Networks. NeurIPS 2021: 23321-23333 - [c47]Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang:
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. NeurIPS Datasets and Benchmarks 2021 - [c46]Yu Zhang, Zhihong Shen, Yuxiao Dong, Kuansan Wang, Jiawei Han:
MATCH: Metadata-Aware Text Classification in A Large Hierarchy. WWW 2021: 3246-3257 - [e4]Yuxiao Dong, Dunja Mladenic, Craig Saunders:
Machine Learning and Knowledge Discovery in Databases: Applied Data Science Track - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part IV. Lecture Notes in Computer Science 12460, Springer 2021, ISBN 978-3-030-67666-7 [contents] - [e3]Yuxiao Dong, Georgiana Ifrim, Dunja Mladenic, Craig Saunders, Sofie Van Hoecke:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science and Demo Track - European Conference, ECML PKDD 2020, Ghent, Belgium, September 14-18, 2020, Proceedings, Part V. Lecture Notes in Computer Science 12461, Springer 2021, ISBN 978-3-030-67669-8 [contents] - [e2]Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, José Antonio Lozano:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part IV. Lecture Notes in Computer Science 12978, Springer 2021, ISBN 978-3-030-86513-9 [contents] - [e1]Yuxiao Dong, Nicolas Kourtellis, Barbara Hammer, José Antonio Lozano:
Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track - European Conference, ECML PKDD 2021, Bilbao, Spain, September 13-17, 2021, Proceedings, Part V. Lecture Notes in Computer Science 12979, Springer 2021, ISBN 978-3-030-86516-0 [contents] - [i32]Yu Zhang, Zhihong Shen, Yuxiao Dong, Kuansan Wang, Jiawei Han:
MATCH: Metadata-Aware Text Classification in A Large Hierarchy. CoRR abs/2102.07349 (2021) - [i31]Fanjin Zhang, Jie Tang, Xueyi Liu, Zhenyu Hou, Yuxiao Dong, Jing Zhang, Xiao Liu, Ruobing Xie, Kai Zhuang, Xu Zhang, Leyu Lin, Philip S. Yu:
Understanding WeChat User Preferences and "Wow" Diffusion. CoRR abs/2103.02930 (2021) - [i30]Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, Jure Leskovec:
OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs. CoRR abs/2103.09430 (2021) - [i29]Yian Yin, Yuxiao Dong, Kuansan Wang, Dashun Wang, Benjamin F. Jones:
Science as a Public Good: Public Use and Funding of Science. CoRR abs/2105.00152 (2021) - [i28]Xu Zou, Qinkai Zheng, Yuxiao Dong, Xinyu Guan, Evgeny Kharlamov, Jialiang Lu, Jie Tang:
TDGIA: Effective Injection Attacks on Graph Neural Networks. CoRR abs/2106.06663 (2021) - [i27]Bo Chen, Jing Zhang, Xiaokang Zhang, Yuxiao Dong, Jian Song, Peng Zhang, Kaibo Xu, Evgeny Kharlamov, Jie Tang:
GCCAD: Graph Contrastive Coding for Anomaly Detection. CoRR abs/2108.07516 (2021) - [i26]Qinkai Zheng, Xu Zou, Yuxiao Dong, Yukuo Cen, Da Yin, Jiarong Xu, Yang Yang, Jie Tang:
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning. CoRR abs/2111.04314 (2021) - [i25]Qingsong Lv, Ming Ding, Qiang Liu, Yuxiang Chen, Wenzheng Feng, Siming He, Chang Zhou, Jianguo Jiang, Yuxiao Dong, Jie Tang:
Are we really making much progress? Revisiting, benchmarking, and refining heterogeneous graph neural networks. CoRR abs/2112.14936 (2021) - 2020
- [j8]Anshul Kanakia, Kuansan Wang, Yuxiao Dong, Boya Xie, Kyle Lo, Zhihong Shen, Lucy Lu Wang, Chiyuan Huang, Darrin Eide, Sebastian Kohlmeier, Chieh-Han Wu:
Mitigating Biases in CORD-19 for Analyzing COVID-19 Literature. Frontiers Res. Metrics Anal. 5: 596624 (2020) - [j7]Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Yuxiao Dong, Anshul Kanakia:
Microsoft Academic Graph: When experts are not enough. Quant. Sci. Stud. 1(1): 396-413 (2020) - [c45]Yuxiao Dong, Ziniu Hu, Kuansan Wang, Yizhou Sun, Jie Tang:
Heterogeneous Network Representation Learning. IJCAI 2020: 4861-4867 - [c44]Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang:
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. KDD 2020: 1150-1160 - [c43]Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun:
GPT-GNN: Generative Pre-Training of Graph Neural Networks. KDD 2020: 1857-1867 - [c42]Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Evgeny Kharlamov, Jie Tang:
Graph Random Neural Networks for Semi-Supervised Learning on Graphs. NeurIPS 2020 - [c41]Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec:
Open Graph Benchmark: Datasets for Machine Learning on Graphs. NeurIPS 2020 - [c40]Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun:
Heterogeneous Graph Transformer. WWW 2020: 2704-2710 - [i24]Ziniu Hu, Yuxiao Dong, Kuansan Wang, Yizhou Sun:
Heterogeneous Graph Transformer. CoRR abs/2003.01332 (2020) - [i23]Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, Jure Leskovec:
Open Graph Benchmark: Datasets for Machine Learning on Graphs. CoRR abs/2005.00687 (2020) - [i22]Wenzheng Feng, Jie Zhang, Yuxiao Dong, Yu Han, Huanbo Luan, Qian Xu, Qiang Yang, Jie Tang:
Graph Random Neural Network. CoRR abs/2005.11079 (2020) - [i21]Jiezhong Qiu, Qibin Chen, Yuxiao Dong, Jing Zhang, Hongxia Yang, Ming Ding, Kuansan Wang, Jie Tang:
GCC: Graph Contrastive Coding for Graph Neural Network Pre-Training. CoRR abs/2006.09963 (2020) - [i20]Ziniu Hu, Yuxiao Dong, Kuansan Wang, Kai-Wei Chang, Yizhou Sun:
GPT-GNN: Generative Pre-Training of Graph Neural Networks. CoRR abs/2006.15437 (2020) - [i19]Scott Freitas, Yuxiao Dong, Joshua Neil, Duen Horng Chau:
A Large-Scale Database for Graph Representation Learning. CoRR abs/2011.07682 (2020)
2010 – 2019
- 2019
- [j6]Kuansan Wang, Zhihong Shen, Chiyuan Huang, Chieh-Han Wu, Darrin Eide, Yuxiao Dong, Junjie Qian, Anshul Kanakia, Alvin Chen, Richard Rogahn:
A Review of Microsoft Academic Services for Science of Science Studies. Frontiers Big Data 2: 45 (2019) - [c39]Jie Zhang, Yuxiao Dong, Yan Wang, Jie Tang, Ming Ding:
ProNE: Fast and Scalable Network Representation Learning. IJCAI 2019: 4278-4284 - [c38]Fanjin Zhang, Xiao Liu, Jie Tang, Yuxiao Dong, Peiran Yao, Jie Zhang, Xiaotao Gu, Yan Wang, Bin Shao, Rui Li, Kuansan Wang:
OAG: Toward Linking Large-scale Heterogeneous Entity Graphs. KDD 2019: 2585-2595 - [c37]Xiao Huang, Peng Cui, Yuxiao Dong, Jundong Li, Huan Liu, Jian Pei, Le Song, Jie Tang, Fei Wang, Hongxia Yang, Wenwu Zhu:
Learning From Networks: Algorithms, Theory, and Applications. KDD 2019: 3221-3222 - [c36]Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh V. Chawla:
Neural Tensor Factorization for Temporal Interaction Learning. WSDM 2019: 537-545 - [c35]Jie Tang, Yuxiao Dong:
Representation Learning on Networks: Theories, Algorithms, and Applications. WWW (Companion Volume) 2019: 1321-1322 - [c34]Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang:
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. WWW 2019: 1509-1520 - [i18]Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Chi Wang, Kuansan Wang, Jie Tang:
NetSMF: Large-Scale Network Embedding as Sparse Matrix Factorization. CoRR abs/1906.11156 (2019) - 2018
- [j5]Hong Huang, Yuxiao Dong, Jie Tang, Hongxia Yang, Nitesh V. Chawla, Xiaoming Fu:
Will Triadic Closure Strengthen Ties in Social Networks? ACM Trans. Knowl. Discov. Data 12(3): 30:1-30:25 (2018) - [c33]Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Louis Faust, Nitesh V. Chawla:
RESTFul: Resolution-Aware Forecasting of Behavioral Time Series Data. CIKM 2018: 1073-1082 - [c32]Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang:
DeepInf: Social Influence Prediction with Deep Learning. KDD 2018: 2110-2119 - [c31]Xian Wu, Yuxiao Dong, Baoxu Shi, Ananthram Swami, Nitesh V. Chawla:
Who will Attend This Event Together? Event Attendance Prediction via Deep LSTM Networks. SDM 2018: 180-188 - [c30]Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang:
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. WSDM 2018: 459-467 - [c29]Jie Tang, Michalis Vazirgiannis, Yuxiao Dong, Fragkiskos D. Malliaros, Michael Cochez, Mayank Kejriwal, Achim Rettinger:
BigNet 2018 Chairs' Welcome & Organization. WWW (Companion Volume) 2018: 943-944 - [i17]Xian Wu, Baoxu Shi, Yuxiao Dong, Chao Huang, Nitesh V. Chawla:
Neural Tensor Factorization. CoRR abs/1802.04416 (2018) - [i16]Yuxiao Dong, Hao Ma, Jie Tang, Kuansan Wang:
Collaboration Diversity and Scientific Impact. CoRR abs/1806.03694 (2018) - [i15]Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, Jie Tang:
DeepInf: Social Influence Prediction with Deep Learning. CoRR abs/1807.05560 (2018) - 2017
- [j4]Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, Yang Yang:
User Modeling on Demographic Attributes in Big Mobile Social Networks. ACM Trans. Inf. Syst. 35(4): 35:1-35:33 (2017) - [c28]Xian Wu, Yuxiao Dong, Jun Tao, Chao Huang, Nitesh V. Chawla:
Reliable fake review detection via modeling temporal and behavioral patterns. IEEE BigData 2017: 494-499 - [c27]Yuxiao Dong, Nitesh V. Chawla, Ananthram Swami:
metapath2vec: Scalable Representation Learning for Heterogeneous Networks. KDD 2017: 135-144 - [c26]Yuxiao Dong, Reid A. Johnson, Jian Xu, Nitesh V. Chawla:
Structural Diversity and Homophily: A Study Across More Than One Hundred Big Networks. KDD 2017: 807-816 - [c25]Yuxiao Dong, Hao Ma, Zhihong Shen, Kuansan Wang:
A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations. KDD 2017: 1437-1446 - [c24]Xian Wu, Yuxiao Dong, Chao Huang, Jian Xu, Dong Wang, Nitesh V. Chawla:
UAPD: Predicting Urban Anomalies from Spatial-Temporal Data. ECML/PKDD (2) 2017: 622-638 - [i14]Yuxiao Dong, Hao Ma, Zhihong Shen, Kuansan Wang:
A Century of Science: Globalization of Scientific Collaborations, Citations, and Innovations. CoRR abs/1704.05150 (2017) - [i13]Jiezhong Qiu, Yuxiao Dong, Hao Ma, Jian Li, Kuansan Wang, Jie Tang:
Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec. CoRR abs/1710.02971 (2017) - 2016
- [j3]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Can Scientific Impact Be Predicted? IEEE Trans. Big Data 2(1): 18-30 (2016) - [c23]Ashwin Bahulkar, Boleslaw K. Szymanski, Omar Lizardo, Yuxiao Dong, Yang Yang, Nitesh V. Chawla:
Analysis of link formation, persistence and dissolution in NetSense data. ASONAM 2016: 1197-1204 - [c22]Siddharth Pal, Yuxiao Dong, Bishal Thapa, Nitesh V. Chawla, Ananthram Swami, Ram Ramanathan:
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User Modeling in Large Social Networks. WSDM 2016: 713 - [i12]