2017

• [TKDE]

  Multi-instance learning with discriminative bag mapping Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Xindong Wu IEEE Transactions on Knowledge and Data Engineering (TKDE) [bib] [dataset]

• [TNNLS]

  Multiple Structure-View Learning for Graph Classification Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Philip S. Yu IEEE Transactions on Neural Networks and Learning Systems (TNNLS) [bib]

• [TCYB]

  Positive and Unlabeled Multi-Graph Learning Jia Wu, Shirui Pan, Xingquan Zhu, Chengqi Zhang, and Xindong Wu IEEE Transactions on Cybernetics (TCYB) [paper] [bib]

• [TKDD]

  Combining Structured Node Content and Topology Information for Networked Graph Clustering Ting Guo, Jia Wu, Xingquan Zhu, and Chengqi Zhang ACM Transactions on Knowledge Discovery from Data (TKDD) [paper] [bib]

• [TCYB]

  Task Sensitive Feature Exploration and Learning for Multitask Graph Classification Shirui Pan, Jia Wu, Xingquan Zhu, Guodong Long, and Chengqi Zhang IEEE Transactions on Cybernetics (TCYB) [paper] [bib]

• [TCYB]

  Stacked convolutional denoising auto-encoders for feature representation Bo Du, Wei Xiong, Jia Wu, Lefei Zhang, and Dacheng Tao IEEE Transactions on Cybernetics (TCYB) [paper] [bib]

• [PR]

  Instance Cloned Extreme Learning Machine Yongshan Zhang, Jia Wu, Zhihua Cai, and Chuan Zhou Pattern Recognition (PR) [paper] [bib]

• [PR]

  LSH Encoding for Multi-instance Learning Dongkuan Xu, Jia Wu, Dewei Li, Yingjie Tian, Xingquan Zhu, and Xindong Wu Pattern Recognition (PR) [paper] [bib]

• [KAIS]

  Boosting for graph classification with universumg Shirui Pan, Jia Wu, Xingquan Zhu, Guodong Long, Chengqi Zhang Knowledge and Information Systems (KAIS) [paper] [bib]

• [KAIS]

  Towards Value Difference Metric with Attribute Weighting Chaoqun Li, Liangxiao Jiang, Hongwei Li, Jia Wu, Peng Zhang Knowledge and Information Systems (KAIS) [paper] [bib]

• [CN]

  Towards large-scale social networks with online diffusion provenance detection Haishuai Wang, Jia Wu, Shirui Pan, Peng Zhang, and Ling Chen Computer Networks (CN) [paper] [bib]

• [IDD]

  Graph-theoretic node importance mining in world city networks: methods and applications Shan Xue, Li Xiong, Zhao Lu, Jia Wu Information Discovery and Delivery (IDD) [paper] [bib]

• [IEEE IS]

  Collective Hyping Detection System for Identifying Online Spam Activities Qinzhe Zhang, Jia Wu, Peng Zhang, Guodong Long, Chengqi Zhang IEEE Intelligent Systems (IEEE IS) [paper] [bib]

• [AAAI]

  Collaborative Dynamic Sparse Topic Regression with User Profile Evolution for Item Recommendation Li Gao, Jia Wu, Chuan Zhou, and Yue Hu AAAI Conference on Artificial Intelligence (AAAI) [paper] [bib]

• [IJCNN]

  CPMF: A Collective Pairwise Matrix Factorization Model for Upcoming Event Recommendation Chun-Yi Liu, Chuan Zhou, Jia Wu, Hongtao Xie, Yue Hu, Li Guo IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib] [*Best Student Paper Award*]

2016

• [TKDE]

  Joint Structure Feature Exploration and Regularization for Multi-Task Graph Classification Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, and Philip S. Yu IEEE Transactions on Knowledge and Data Engineering (TKDE) [paper] [bib]

• [PR]

  SODE: Self-Adaptive One-Dependence Estimators for Classification Jia Wu, Shirui Pan, Xingquan Zhu, Peng Zhang, Chengqi Zhang Pattern Recognition (PR) [paper] [bib]

• [PR]

  Memetic Extreme Learning Machine Yongshan Zhang, Jia Wu, Zhihua Cai, Peng Zhang, Ling Chen Pattern Recognition (PR) [paper] [bib]

• [KAIS]

  Multi-graph-view subgraph mining for graph classification Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang Knowledge and Information Systems (KAIS) [paper] [bib]

• [KBS]

  Big social network influence maximization via recursively estimating influence spread Weixue Lu, Chuan Zhou, Jia Wu Knowledge Based Systems (KBS) [paper] [bib]

• [AAAI]

  On the Minimum Differentially Resolving Set Problem for Diffusion Source Inference in Networks Chuan Zhou, Weixue Lu, Peng Zhang, Jia Wu, Yue Hu, Li Guo AAAI Conference on Artificial Intelligence (AAAI) [paper] [bib]

• [CIKM]

  Global and Local Influence-based Social Recommendation Qinzhe Zhang, Jia Wu, Hong Yang, Weixue Lu, Guodong Long, Chengqi Zhang ACM International Conference on Information and Knowledge Management (CIKM) [paper] [bib]

• [CIKM]

  Collaborative Social Group Influence for Event Recommendation Li Gao, Jia Wu, Zhi Qiao, Chuan Zhou, Hong Yang, Yue Hu ACM International Conference on Information and Knowledge Management (CIKM) [paper] [bib]

• [DASFAA]

  Multiple-Instance Learning with Evolutionary Instance Selection Yongshan Zhang, Jia Wu, Chuan Zhou, Peng Zhang, Zhihua Cai International Conference on Database Systems for Advanced Applications (DASFAA) [paper] [bib]

• [ICDM]

  Inferring Latent Network from Cascade Data for Dynamic Social Recommendation Qin Zhang, Jia Wu, Peng Zhang, Guodong Long, Ivor W. Tsang, Chengqi Zhang International Conference on Database Systems for Advanced Applications (ICDM) [paper] [bib]

• [IJCAI]

  Semi-Data-Driven Network Coarsening Li Gao, Jia Wu, Hong Yang, Zhi Qiao, Chuan Zhou, Yue Hu International Joint Conference on Artificial Intelligence (IJCAI) [paper] [bib]

• [IJCAI]

  Tri-Party Deep Network Representation Shirui Pan, Jia Wu, Xingquan Zhu, Chengqi Zhang, Yang Wang International Joint Conference on Artificial Intelligence (IJCAI) [paper] [bib]

• [IJCAI]

  Unsupervised Feature Learning from Time Series Qin Zhang, Jia Wu, Hong Yang, Yingjie Tian, Chengqi Zhang International Joint Conference on Artificial Intelligence (IJCAI) [paper] [bib]

• [IJCNN]

  Evolutionary lazy learning for Naive Bayes classification Yu Bai, Haishuai Wang, Jia Wu, Yun Zhang, Jing Jiang, Guodong Long IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]

• [IJCNN]

  Training deep neural networks on imbalanced data sets Shoujin Wang, Wei Liu, Jia Wu, Longbing Cao, Qinxue Meng, Paul J. Kennedy IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]

• [IJCNN]

  Multi-instance graphical transfer clustering for traffic data learning Shan Xue, Jie Lu, Jia Wu, Guangquan Zhang, Li Xiong IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]

• [IJCNN]

  A recursive method for big network influence estimation Weixue Lu, Chuan Zhou, Jia Wu, Chun-Yi Liu, Li Gao, Yue Hu IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]

• [IJCNN]

  Mining subcascade features for cascade outbreak prediction in big networks Haishuai Wang, Jia Wu, Chuan Zhou, Zhenyan Ji, Jun Wu IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]

2015

• [TCYB]

  Boosting for Multi-Graph Classification Jia Wu, Shirui Pan, Xingquan Zhu, and Zhihua Cai IEEE Transactions on Cybernetics (TCYB) [paper] [bib]

• [TKDE]

  CogBoost: Boosting for Fast Cost-sensitive Graph Boosting Algorithm Shirui Pan, Jia Wu, and Xingquan Zhu IEEE Transactions on Knowledge and Data Engineering (TKDE) [paper] [bib]

• [TCYB]

  Graph Ensemble Boosting for Imbalanced and Noisy Graph Stream Classification Shirui Pan, Jia Wu, Xingquan Zhu, and Chengqi Zhang IEEE Transactions on Cybernetics (TCYB) [paper] [bib]

• [PR]

  Finding the Best Not the Most: Regularized Loss Minimization Subgraph Selection for Graph Classification Shirui Pan, Jia Wu, Guodong Long, Xingquan Zhu, Chengqi Zhang Pattern Recognition (PR)) [paper] [bib]

• [ESWA]

  Self-adaptive attribute weighting for Naive Bayes classification Jia Wu, Shirui Pan, Xingquan Zhu, Zhihua Cai, Peng Zhang, Chengqi Zhang Expert Systems with Applications (ESWA) [paper] [bib]

• [IJCAI]

  Multi-Graph-View Learning for Complicated Object Classification Jia Wu, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang International Joint Conference on Artificial Intelligence (IJCAI) [paper] [bib]

• [ICONIP]

  Mining Top-k Minimal Redundancy Frequent Patterns over Uncertain Databases Haishuai Wang, Peng Zhang, Jia Wu, Shirui Pan International Conference on Neural Information Processing (ICONIP) [paper] [bib]

• [IJCNN]

  A memetic algorithm based extreme learning machine for classification Yongshan Zhang, Zhihua Cai, Jia Wu, Xinxin Wang, Xiaobo Liu IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]

2014

• [TKDE]

  Bag Constrained Structure Pattern Mining for Multi-Graph Classification Jia Wu, Xingquan Zhu, Chengqi Zhang, and Philip S. Yuu IEEE Transactions on Knowledge and Data Engineering (TKDE) [paper] [bib]

• [ICDM]

  Multi-graph-view Learning for Graph Classification Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang IEEE International Conference on Data Mining (ICDM) [paper] [bib]

• [CIKM]

  Exploring Features for Complicated Objects: Cross-View Feature Selection for Multi-Instance Learning Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Zhihua Cai, Chengqi Zhang ACM International Conference on Conference on Information and Knowledge Management (CIKM) [paper] [bib]

• [SDM]

  Multi-Graph Learning with Positive and Unlabeled Bags Jia Wu, Zhibin Hong, Shirui Pan, Xingquan Zhu, Chengqi Zhang, Zhihua Cai SIAM International Conference on Data Mining (SDM) [paper] [bib]

• [PAKDD]

  Multi-Instance Learning from Positive and Unlabeled Bags Jia Wu, Xingquan Zhu, Chengqi Zhang, Zhihua Cai Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD) [paper] [bib]

• [IJCNN]

  Dual instance and attribute weighting for Naive Bayes classification Jia Wu, Shirui Pan, Zhihua Cai, Xingquan Zhu, Chengqi Zhang IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]

• [IJCNN]

  Attribute weighting: How and when does it work for Bayesian Network Classification Jia Wu, Zhihua Cai, Shirui Pan, Xingquan Zhu, Chengqi Zhang IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]

2013

• [ICDM]

  Multi-instance Multi-graph Dual Embedding Learning Jia Wu, Xingquan Zhu, Chengqi Zhang, Zhihua Cai IEEE International Conference on Data Mining (ICDM) [paper] [bib]

• [IJCNN]

  Self-adaptive probability estimation for Naive Bayes classification Jia Wu, Zhihua Cai, Xingquan Zhu IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]

• [IJCNN]

  Artificial immune system for attribute weighted Naive Bayes classification Jia Wu, Zhihua Cai, Sanyou Zeng, Xingquan Zhu IEEE International Joint Conference on Neural Networks (IJCNN) [paper] [bib]