PENG ZHANG
University of Technology Sydney
CB11.10.107

Phone: +61 2 9514 7204

Email: Peng.Zhang@uts.edu.au

 

 

Short Bio

Peng Zhang received his PhD degree in July 2009 from the Chinese Academy of Sciences. Since then, he has worked with a national laboratory at the Chinese Academy of Sciences as an Assistant/Associate professor (2009-2013). He won the Best Paper Award at ICCS-14 (ERA Rank A) held in Queensland, Australia. In January 2014, he joined the QCIS research center, University of Technology Sydney (UTS), as a Lecturer.

Dr. Zhang is Associate Editor of two Springer journals, Journal of Big Data, and Annals of Data Science. To date, he has published 55 ERA Rank A papers, in which 40+ are in data mining. He continuously serves as a PC member (reviewer) of IEEE TKDE, KDD, ICDM, IJCAI, etc. He organised five workshops in data mining and co-organised two ERA Rank A conferences (ICDM-07 and ICCS-07). This year, he is co-organising an international conference on data science at UTS (ICDS-15) in conjunction with KDD-15 (http://ic-datascience.org/icds2015/).


Selected papers (55 ERA Rank A*/A conference and journal papers, 40+ in data mining.)

  • (TOIT-15) P. Zhang, J. He, G. Long, G. Huang, and C. Zhang, Towards Anomalous Diffusion Sources Detection in a Large Network. ACM Transactions on Internet Technology, Accepted. (ERA Rank A)
  • (CIKM-15) P. Wang, P. Zhang, C. Zhou, W. Feng, and L. Guo. Modeling Infinite Topics on Social Behavior Data with Spatio-temporal Dependence, In Proc. of the 24th ACM Int. Conf. on Information and Knowledge Management, 2015. (ERA Rank A)
  • (CIKM-15) H. Wang, P. Zhang, L. Chen, I. Tsang, C. Zhang, Defragging Subgraph Features for Graph Classification, In Proc. of the 24th ACM International Conference on Information and Knowledge Management, 2015. (ERA Rank A)
  • (IJCAI-15) W. Lu, P. Zhang, C. Zhou, C. Liu and L. Gao. Influence Maximization in Big Network: An Incremental Algorithm for Streaming Subgraph Influence Spread Estimation. In Proc. of the 2015 International Joint on Artificial Intelligence, August 25-31, 2015. [PDF] (ERA Rank A)
  • (TKDE-15) P. Zhang, C. Zhou, P. Wang, B. Gao, X. Zhu, and L. Guo, E-Tree: An Efficient Indexing Structure for Ensemble Models on Data Streams. IEEE Transactions on Knowledge and Data Engineering, Vol. 26 (3), 2015. [codes] (ERA Rank A)
  • (TKDE-15) C. Zhou, P. Zhang, W. Zang, and L. Guo. On the Upper Bounds of Spread for Greedy Algorithms in Social Network Influence Maximization. IEEE Transactions on Knowledge and Data Engineering, 2015 [PDF] (ERA Rank A)
  • (IJCNN-15) H. Wang, P. Zhang, L. Chen, L. Huan and C. Zhang. Online Diffusion Source Detection in Social Networks. In Proceedings of the 2015 International Joint Conference on Neural Networks, 2015. [PDF] (ERA Rank A)
  • (WWW-15) W. Feng, P. Wang, C. Zhou, P. Zhang, and L. Guo, Fast Search for Distance Dependent Chinese Restaurant Processes, In Proceedings of the 24rd ACM International World Wide Web Conference, Italy, 2015. (accpeted poster) (ERA Rank A)
  • (WWW-15) Wenyu Zang, Peng Zhang, Chuan Zhou, and Li Guo, Topic-aware Source Locating in Social Networks, In Proceedings of the 24rd ACM International World Wide Web Conference, Italy, 2015. (accpeted poster) (ERA Rank A)
  • (WWW-15) X. Ji, M. Xu, P. Zhang, C. Zhou, Z. Qiao and L. Guo, Online Event Recommendation for Event-based Social Networks, In Proceedings of the 24rd ACM International World Wide Web Conference, Italy, 2015. (accpeted poster) (ERA Rank A)
  • (PAKDD-15) P. Wang, C. Zhou, P. Zhang, W. Feng, L. Guo, B. Fang: Evolving Chinese Restaurant Processes for Modeling Evolutionary Traces in Temporal Data. In Proc. of the 14th Pacific-Asia Conf. on Knowledge Discovery and Data Mining, 2015, pages: 79-91. (ERA Rank A)
  • (ICDM-14) Q. Zhi, P. Zhang, W. Niu, C. Zhou, L. Guo. Online Nonparametric Max-Margin Matrix Factorization for Collaborative Prediction. In Proceedings of the 13th IEEE International Conference on Data Mining, December 7-10, 2014. [PDF] (ERA Rank A)
  • (PAKDD-14) P. Wang, P. Zhang, L. Guo, Forward Prediction on Data Streams, In Proceedings of the 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining , 2014. (ERA Rank A)
  • (WWW-14) Z. Qiao, P. Zhang, J. He, Y. Cao, C. Zhou and L. Guo, Combining Geographical Information of Users and Content of Items for Accurate Rating Prediction, In Proceedings of the 23rd ACM International World Wide Web Conference, Korea, 2014. (ERA Rank A)
  • (ICCS-14) Z. Qiao, P. Zhang, Y, Cao, C, Zhou, L, Guo. Improving Collaborative Recommendation via Location-based User-Item Subgroup. In Proceedings of the 14th International Conference on Computational Science, Cairns, Australia. (ERA Rank A)
  • (ICCS-14) Z. Tan, P. Zhang, J. Tan, L. Guo. A Multi-layer Event Detection Algorithm for Detecting Global and Local Hot Events in Social Networks. In Proceedings of the 14th International Conference on Computational Science, Cairns, Australia. (ERA Rank A)
  • (AAAI-14) Q. Zhi, P. Zhang, Y. Cao, C. Zhou, L. Guo and B. Fang. Combining Heterogenous Social and Geographical Information for Event Recommendation, In Proceedings of the 28th AAAI Conference on Artificial Intelligence, July 27-31, 2014, Quebec City, Canada. (ERA Rank A)
  • (AAAI-14) Z. Qiao, P. Zhang, C. Zhou, Y. Cao, L. Guo, Yanchuan Zhang: Event Recommendation in Event-Based Social Networks. In Proceedings of the 28th AAAI Conference on Artificial Intelligence, July 27-31, 2014, Quebec City, Canada. (ERA Rank A)
  • (ICCS-14) C. Zhou, P. Zhang, W. Zang, L. Guo. Maximizing the Cumulative Influence through a Social Network when Repeat Activation Exists. In Proceedings of the 14th International Conference on Computational Science, Cairns, Australia. (ERA Rank A)
  • (ICCS-14) Y. Cao, P. Zhang, J. Guo, L. Guo. Mining Large-scale Event Knowledge from Web Text. In Proceedings of the 14th International Conference on Computational Science, Cairns, Australia, May 2014.(ERA Rank A)
  • (ICCS-14) W. Zang, P. Zhang, C. Zhou, L. Guo. Discovering Multiple Diffusion Source Nodes in Social Networks. In Proceedings of the 14th International Conference on Computational Science, Cairns, Australia. [Best Paper Award] (ERA Rank A)
  • (WWW-14) C. Zhou, P. Zhang, W. Zang and L. Guo, Maximizing the Long-term Integral Influence in Social Networks under the Voter Model, In Proceedings of the 23rd ACM International World Wide Web Conference, Korea, 2014.(ERA Rank A)
  • (WWW-14) C. Zhou, P. Zhang, J. Guo and L. Guo, Upper Bound based Greedy Algorithm for Mining Top-k Influential Nodes in Social Networks, In Proceedings of the 23rd ACM International World Wide Web Conference, Korea, 2014. (poster) (ERA Rank A)
  • (CIKM-13) J. Guo, P. Zhang, C. Zhou, Y.Cao and L. Guo. Personalized Influence Maximization on Social Networks. In Proceedings of the 22nd International Conference on Information and Knowledge Management, October 28- November 1, 2013, San Francisco, CA, USA.  [MP3, PPT, PDF] (ERA Rank A)
  • (ICDM-13) C. Zhou, P. Zhang, J. Guo, X. Zhu and L. Guo, UBLF: An upper bound based approach to discover influential nodes in  social networks. In Proceedings of the 13th IEEE International Conference on Data Mining, December 7-10, 2013, Dallas, TX, USA. [PDF , PPT] (ERA Rank A)
  • (PAKDD-13) Z. Qiao, G. Huang, J. He, P. Zhang, L. Guo, J. Cao, and Y. Zhang, Discovering Semantics from Multiple Correlated Time Series Stream. In Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining , Australia, April 14-17, 2013.(ERA Rank A)
  • (ICDM-12) J. Li, P. Zhang, Y. Cao, P. Liu, and L. Guo, Efficient Behavior Targeting Using Ensmeble SVM Indexing. In Proceedings of the 12th IEEE International Conference on Data Mining, Dec. 09-13, 2012, Brussels, Belgium. [PDF] (ERA Rank A)
  • (SDM-12) P. Wang, P. Zhang, and L. Guo, Minining Multi-label Data Streams Using Ensemble-based Active Learning . In Proceedings of the 2012 SIAM International Conference on Data Mining, Anaheim, California, USA. (ERA Rank A)
  • (ICCS-12) J. Guo, P. Zhang, J. Tan, L. Guo. Mining Hot Topics from Twitter Streams. In Proceedings of the 12th International Conference on Computational Science , Omaha, USA. (ERA Rank A)
  • (DSS-11) P. Zhang, X. Zhu, Y. Shi, L. Guo, and X. Wu, Robust Ensemble Learning for Mining Noisy Data Streams, Decision Support Systems, Vol. 50(2), 2011, pages: 469-479. (ERA Rank A*)
  • (KDD-11) P. Zhang, J. Li, P. Wang, B. Gao, X. Zhu, and L. Guo, Enabling Fast Prediction for Ensemble Models on Data Streams. In Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining , August 21-24, 2011, San Diego, CA, USA. [PDF, PPT] (ERA Rank A)
  • (ICDM-11) P. Zhang, B. Gao, X. Zhu, and L. Guo, Enabling Fast Lazy Learning for Data Streams. In Proceedings of the 11th IEEE International Conference on Data Mining , December 11-14, 2011, Vancouver, Canada. [PDF] (ERA Rank A)
  • (CIKM-11) J. Guo, P. Zhang, J. Tan, and L. Guo, Mining Frequent Patterns across Multiple Data Streams. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management , October 24-28, 2011, Glasgow, Scotland, UK. (ERA Rank A)
  • (CIKM-11) J. Li, P. Zhang, J. Tan, P. Liu, and L. Guo, Continuous Data Stream Query in the Cloud. In Proceedings of the 20th ACM International Conference on Information and Knowledge Management , October 24-28, 2011, Glasgow, Scotland, UK. (ERA Rank A)
  • (TSMC-B) X. Zhu, P. Zhang, X. Lin, Y. Shi, Active Learning from Stream Data Using Optimal Weight Classifier Ensemble, IEEE Transactions on System, Man, Cybernetics, Part B, Vol. 40 (6),2010, pages: 1607-1621. (ERA Rank A)
  • (ICDM-10) P. Zhang, X. Zhu, J. Tan, and L. Guo, Classifier and Cluster Ensembles for Mining Concept Drifting Data Streams, In Proceedings of the 10th IEEE International Conference on Data Mining , Sydney, Australia, December 14-17, 2010, pages: 1175-1180.(ERA Rank A)
  • (CIKM-10) P. Zhang, X. Zhu, J. Tan, and L. Guo, SKIF: A Data Imputation Framework for Concept Drifting Data Streams, In Proceedings of the 19th ACM International Conference on Information and Knowledge Management , Toronto, Ontario, Canada, October26-31, pages: 1869-1872. (ERA Rank A)
  • (ICCS-10) Y. Zhang, P. Zhang, L. Zhang, and Y. Shi. Knowledge Extraction from Multiple Criteria Linear Programming Classification Approach, the 10th International Conference on Computational Science, 2010.  (ERA Rank A)
  • (ICDM-09) P. Zhang, X. Zhu, and L. Guo, Mining Data Streams with Labeled and Unlabeled Training Examples. In Proceedings of IEEE International Conference on Data Mining , Miami, Florida, USA, December 6-9, 2009, pages: 627-636. (ERA Rank A)
  • (PAKDD-09) P. Zhang, X. Zhu, Y. Shi, and X. Wu, An Aggregate Ensemble for Mining Concept Drifting Data Streams with Noise. In Proceedings of the 13th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Bangkok, Thailand, April 27-30, 2009, pages: 1021-1029. (ERA Rank A)
  • (ICCS-09) P. Zhang, X. Zhu, and Y. Shi. Bias-Variance Analysis for Ensembling Regularized Multiple Criteria Linear Programming, International Conference on Computational Science , Baton Rouge, Louisiana, USA, May 25-27, 2009. (ERA Rank A)
  • (KDD-08) P. Zhang, X. Zhu, and Y. Shi, Categorizing and Mining Concept Drifting Data Streams. In Proceedings of the 14th ACM international conference on Knowledge Discovery and Data mining, Las Vegas, Nevada, USA, August 24-27, 2008, pages: 812-820. (ERA Rank A)
  • (ICDM-08) X. Zhu, P. Zhang, X. Wu, D. He, C. Zhang, and Y. Shi, Cleansing Noisy Data Streams. In Proceedings of the 8th IEEE International Conference on Data Mining , Pisa, Italy, December 15-19, 2008, pages: 1139-1144. (ERA Rank A)
  • (ICDM-07) X. Zhu, P. Zhang, X. Lin and Y. Shi, Active Learning from Data Streams, In Proceedings of the 7th IEEE International Conference on Data Mining , Omaha, Nebraska, USA, October 28-31, 2007, pages: 757-762. (ERA Rank A)
  • (JOTB) R. Chen, Z. Zhang, D. Wu, P. Zhang, X. Zhang, Y. Wang, and Y. Shi, Prediction of protein interaction hot spots using rough set-based multiple criteria linear programming, Journal of Theoretical Biology,Vol. 269 (1), January 2011, Pages 174-180. (ERA Rank A*)
  • (COR) J. Zhang, Y. Shi, and P. Zhang, Several Multi-criteria Programming Methods for Classification. Computers and Operations Research , Vol. 36(3), 2009, pages: 823-836.    (ERA Rank A)            
  • (ICDM-09) X. Zhou, W. Jiang, Y. Tian, P. Zhang, G. Nie, and Y. Shi, A New Kernel-based Classification Algorithm. In Proceedings of IEEE International Conference on Data Mining , Miami, Florida, USA, December 6-9, 2009, pages: 1094-1099. (ERA Rank A)
  • (ICCS-09) G. Nie, G. Wang, P. Zhang, Y. Tian, Y. Shi. Finding the Hidden Pattern of Credit Card Holder's Churn: A Case of China. International Conference on Computational Science , Baton Rouge, Louisiana, USA, May 25-27, 2009, pages: 561-569. (ERA Rank A)
  • (ICCS-09) Y. Wang, P. Zhang, G. Nie, Yong Shi: Multiple Criteria Quadratic Programming for Financial Distress Prediction of the Listed Manufacturing Companies. International Conference on Computational Science , Baton Rouge, Louisiana, USA, May 25-27, 2009, pages: 616-624. (ERA Rank A)
  • (ICCS-09) Y. Zhang, P. Zhang, Y. Shi. Kernel Based Regularized Multiple Criteria Linear Programming Model. International Conference on Computational Science , Baton Rouge, Louisiana, USA, May 25-27, 2009, pages: 625-632. (ERA Rank A)
  • (ICCS-08) P. Zhang, Y. Tian, X. Li, Z. Zhang, and Y. Shi. Select Representative Samples for Regularized Multiple-Criteria Linear Programming Classification. International Conference on Computational Science , Krakow, Poland, June 23-25, 2008, pages: 436-440. (ERA Rank A)
  • (ICCS-08) Z. Zhang, Y. Shi, P. Zhang, and G. Gao. A Rough Set-Based Multiple Criteria Linear Programming Approach for Classification. International Conference on Computational Science, Krakow, Poland, June 23-25, 2008, pages: 476-485. (ERA Rank A)
  • (ICDM-07) P. Zhang and J. Dai, Multiple Criteria Linear Programming for VIP E-Mail Behavior Analysis. Proceedings of the 7th IEEE International Conference on Data Mining, Omaha, Nebraska, USA, October 28-31, 2007, pages: 289-296. (ERA Rank A)
  • (ICDM-07) Y. Shi, Y. Tian, X. Chen and P. Zhang. A Regularized Multiple Criteria Linear Program for Classification, Proceedings of the 7th IEEE International Conference on Data Mining, Omaha, Nebraska, USA, October 28-31, 2007, pages: 253-258. (ERA Rank A)
  • (ICCS-07) P. Zhang, J. Zhang, and Y. Shi. A New Multi-criteria Quadratic-programming Linear Classification Model for VIP E-Mail Analysis, International Conference on Computational Science , Beijing, China, May 27-30, pages: 499-502. (ERA Rank A)

 


    System 1: SocialAnalysis: A Real-time Twitter Stream Query and Mining System

    We designed a real-time system on Twitter to discover and summarize emergent social events from twitter streams. Social events, such as parade and riots, are characterized with a common phenomenon that people always participate in each procedure, which contains preparations, organization and initiation for the event. As the development of social networks, people always frequently post messages or comments about their activities and opinions. Hence, there exist temporal correlations between the physical world and virtual social networks, which can help us to monitor and track social events, detecting and positioning anomaly events before their outbreakings, so as to provide early warning. The key technologies in the systems involve: (1) Data denoising methods based on multi-features, which screens out the related specific event data from massive data. (2) Abnormal events detection methods based on statistical learning, which can detect anomalies by analyzing and mining series of observation and statistics on the time axis. Because time series data have features of high velocity, high noise and nonlinearity, we designed models to calculate the anomaly scores for each time point in order to do fast detection. When the anomaly score at some certain time point is higher than a threshold, the point will be considered as an abnormal node. (3) Geographical position recognition, which is used to recognize regions where abnormal events may happen. Because social events may also occur in multiple, clustering methods such as density based method are used to obtain the approximate geographical locations, and then the maximum likelihood estimation can be used to accurately locate location by considering trusted weights.

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    System 2: OPTMINER: A Multi-Criteria Optimization Toolbox for Classification

    The OPTMINER toolbox was designed for data classification based on multiple criteria mathematical programming. It has been successful applied to credit card portfolio management, bioinformatics, fraud management, intrusion detection and email log data analysis. It realizes the auto-process of data classification by sealing the details of middle operations, including parameter adjustment and the access of intermediate results. Users only need to feed data with format complying with the original data file (database). Users can get the statistical results of the density distribution, KS Value and the parameters. In contrast to OPTMiner1.0, OPTMiner2.0 are more intelligent in recognizing the data source, it can automatically calculate the file / database table size and the number of attributes, so that users don't need to manually input these parameters. OPTMiner2.0 can automatically fill up missing attribute values. When the algorithm encounters an error pop-up warning window, users are suggested to modify the existing source data errors according to the warning information.

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For DOS program here, for Windows Program here. Windows requires Microsoft .NET Framework 2.0.