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2020 – today
- 2022
[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 - [i38]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) - [i37]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) - [i36]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) - [i35]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) - [i34]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) - 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] - [i33]Yu Zhang, Zhihong Shen, Yuxiao Dong, Kuansan Wang, Jiawei Han:
MATCH: Metadata-Aware Text Classification in A Large Hierarchy. CoRR abs/2102.07349 (2021) - [i32]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) - [i31]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) - [i30]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) - [i29]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) - [i28]Xiao Liu, Haoyun Hong, Xinghao Wang, Zeyi Chen, Evgeny Kharlamov, Yuxiao Dong, Jie Tang:
A Self-supervised Method for Entity Alignment. CoRR abs/2106.09395 (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:
Deep learning for network analysis: Problems, approaches and challenges. MILCOM 2016: 588-593 - [c21]Yuxiao Dong:
User Modeling in Large Social Networks. WSDM 2016: 713 - [i12]Yuxiao Dong, Reid A. Johnson, Jian Xu, Nitesh V. Chawla:
Structural Diversity and Homophily: A Study Across More than One Hundred Large-Scale Networks. CoRR abs/1602.07048 (2016) - [i11]Yang Yang, Nitesh V. Chawla, Ryan N. Lichtenwalter, Yuxiao Dong:
Influence Activation Model: A New Perspective in Social Influence Analysis and Social Network Evolution. CoRR abs/1605.08410 (2016) - [i10]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Can Scientific Impact Be Predicted? CoRR abs/1606.05905 (2016) - [i9]Yuxiao Dong, Omar Lizardo, Nitesh V. Chawla:
Do the Young Live in a "Smaller World" Than the Old? Age-Specific Degrees of Separation in a Large-Scale Mobile Communication Network. CoRR abs/1606.07556 (2016) - [i8]Yang Yang, Omar Lizardo, Dong Wang, Yuxiao Dong, Aaron D. Striegel, David Hachen, Nitesh V. Chawla:
Gender Differences in Communication Behaviors, Spatial Proximity Patterns, and Mobility Habits. CoRR abs/1607.06740 (2016) - [i7]Ashwin Bahulkar, Boleslaw K. Szymanski, Omar Lizardo, Yuxiao Dong, Yang Yang, Nitesh V. Chawla:
Analysis of Link Formation, Persistence and Dissolution in NetSense Data. CoRR abs/1611.00568 (2016) - 2015
- [c20]Yuxiao Dong, Reid A. Johnson, Yang Yang, Nitesh V. Chawla:
Collaboration Signatures Reveal Scientific Impact. ASONAM 2015: 480-487 - [c19]Yuxiao Dong, Jing Zhang, Jie Tang, Nitesh V. Chawla, Bai Wang:
CoupledLP: Link Prediction in Coupled Networks. KDD 2015: 199-208 - [c18]Yuxiao Dong, Nitesh V. Chawla, Jie Tang, Yang Yang, Yang Yang:
The Evolution of Social Relationships and Strategies Across the Lifespan. ECML/PKDD (3) 2015: 245-249 - [c17]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Will This Paper Increase Your h-index? ECML/PKDD (3) 2015: 259-263 - [c16]Yuxiao Dong, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, Nitesh V. Chawla:
Inferring Unusual Crowd Events from Mobile Phone Call Detail Records. ECML/PKDD (2) 2015: 474-492 - [c15]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Will This Paper Increase Your h-index?: Scientific Impact Prediction. WSDM 2015: 149-158 - [i6]Yuxiao Dong, Fabio Pinelli, Yiannis Gkoufas, Zubair Nabi, Francesco Calabrese, Nitesh V. Chawla:
Inferring Unusual Crowd Events From Mobile Phone Call Detail Records. CoRR abs/1504.03643 (2015) - [i5]Yang Yang, Jie Tang, Yuxiao Dong, Qiaozhu Mei, Reid A. Johnson, Nitesh V. Chawla:
Modeling the Interplay Between Individual Behavior and Network Distributions. CoRR abs/1511.02562 (2015) - 2014
- [c14]Yuxiao Dong, Yang Yang, Jie Tang, Yang Yang, Nitesh V. Chawla:
Inferring user demographics and social strategies in mobile social networks. KDD 2014: 15-24 - [i4]Yuxiao Dong, Jie Tang, Nitesh V. Chawla, Tiancheng Lou, Yang Yang, Bai Wang:
Inferring social status and rich club effects in enterprise communication networks. CoRR abs/1404.3708 (2014) - [i3]Yang Yang, Yuxiao Dong, Nitesh V. Chawla:
Predicting Node Degree Centrality with the Node Prominence Profile. CoRR abs/1412.2269 (2014) - [i2]Yuxiao Dong, Reid A. Johnson, Nitesh V. Chawla:
Will This Paper Increase Your h-index? Scientific Impact Prediction. CoRR abs/1412.4754 (2014) - 2013
- [j2]Chuan Shi, Yanan Cai, Di Fu, Yuxiao Dong, Bin Wu:
A link clustering based overlapping community detection algorithm. Data Knowl. Eng. 87: 394-404 (2013) - [c13]Yuxiao Dong, Jie Tang, Tiancheng Lou, Bin Wu, Nitesh V. Chawla:
How Long Will She Call Me? Distribution, Social Theory and Duration Prediction. ECML/PKDD (2) 2013: 16-31 - [i1]Yang Yang, Yuxiao Dong, Nitesh V. Chawla:
Microscopic Evolution of Social Networks by Triad Position Profile. CoRR abs/1310.1525 (2013) - 2012
- [c12]Yuxiao Dong, Jie Tang, Sen Wu, Jilei Tian, Nitesh V. Chawla, Jinghai Rao, Huanhuan Cao:
Link Prediction and Recommendation across Heterogeneous Social Networks. ICDM 2012: 181-190 - 2011
- [c11]Yanan Cai, Chuan Shi, Yuxiao Dong, Qing Ke, Bin Wu:
A Novel Genetic Algorithm for Overlapping Community Detection. ADMA (1) 2011: 97-108 - [c10]Qing Ke, Yuxiao Dong, Bin Wu:
Efficient Search in Networks Using Conductance. ASONAM 2011: 37-44 - [c9]Yuxiao Dong, Qing Ke, Bai Wang, Bin Wu:
Link Prediction Based on Local Information. ASONAM 2011: 382-386 - [c8]Bin Wu, Yuxiao Dong, Lei Qin, Qing Ke, Bai Wang:
KANGAROO: A Distributed System for SNA - Social Network Analysis in Huge-scale Networks. CLOSER 2011: 404-409 - [c7]Qing Ke, Bin Wu, Yuxiao Dong, Lei Qin:
Saurida: Cloud Computing based - Data Mining System in Telecommunication Industry. CLOSER 2011: 516-519 - [c6]Yuxiao Dong, Qing Ke, Jun Rao, Bin Wu:
Predicting missing links via local feature of common neighbors. FSKD 2011: 1038-1042 - [c5]Lei Qin, Bin Wu, Qing Ke, Yuxiao Dong:
SAKU: A distributed system for data analysis in large-scale dataset based on cloud computing. FSKD 2011: 1257-1261 - [c4]Bin Wu, Qing Ke, Yuxiao Dong:
Degree and similarity based search in networks. FSKD 2011: 1267-1270 - [c3]Zhendong Liu, Yuxiao Dong, Hengwu Li, Huijian Han:
Approximating algorithm for RNA structure prediction including pseudoknots. ICAL 2011: 325-329 - [c2]Yuxiao Dong, Qing Ke, Jun Rao, Bai Wang, Bin Wu:
Random Walk Based Resource Allocation: Predicting and Recommending Links in Cross-Operator Mobile Communication Networks. ICDM Workshops 2011: 358-365 - [c1]Bin Wu, Yuxiao Dong, Qing Ke, Yanan Cai:
A parallel computing model for large-graph mining with MapReduce. ICNC 2011: 43-47 - 2010
- [j1]Guang Cen, Yuxiao Dong, Wanlin Gao, Lina Yu, Simon See, Qing Wang, Ying Yang, Hongbiao Jiang:
A implementation of an automatic examination paper generation system. Math. Comput. Model. 51(11-12): 1339-1342 (2010)
Coauthor Index