Enabling User-Centric Wisdom Engines for Big Information Network Search

Funding: 2014: $132,000
2015: $120,000
2016: $130,000

Project Member(s): Zhang, C.

Funding or Partner Organisation: Australian Research Council (ARC Discovery Projects)
Arizona State University

Start year: 2014

Summary: Big information networks, e.g. social networks, are important for modern information systems, yet searching for useful information from huge networks is difficult because network structure and user relationships continuously evolve. This project will provide theoretical foundations for structural knowledge mining to enable user-centric wisdom search on big information networks. Expected outcomes are: real-world application platform to support information network analysis; theories for big network control, algorithms, and systematic solutions to enable user-centric knowledge search, including a new search engine for big information networks. By significantly improving IT, it will benefit Australian business, industry and the wider community.

Publications:

Zhang, Q, Wu, J, Zhang, P, Long, G & Zhang, C 2019, 'Salient Subsequence Learning for Time Series Clustering', IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 41, no. 9, pp. 2193-2207.
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Wu, J, Pan, S, Zhou, C, Li, G, He, W & Zhang, C 2018, 'Advances in Processing, Mining, and Learning Complex Data: From Foundations to Real‐World Applications', Complexity, vol. 2018, no. 1, pp. 1-3.
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Wu, J, Pan, S, Zhu, X, Zhang, C & Wu, X 2018, 'Multi-Instance Learning with Discriminative Bag Mapping', IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 6, pp. 1065-1080.
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Zhang, Q, Wu, J, Zhang, Q, Zhang, P, Long, G & Zhang, C 2018, 'Dual influence embedded social recommendation', World Wide Web, vol. 21, no. 4, pp. 849-874.
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Guo, T, Wu, J, Zhu, X & Zhang, C 2017, 'Combining Structured Node Content and Topology Information for Networked Graph Clustering', ACM Transactions on Knowledge Discovery from Data, vol. 11, no. 3, pp. 1-29.
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Pan, S, Wu, J, Zhu, X, Long, G & Zhang, C 2017, 'Boosting for graph classification with universum', Knowledge and Information Systems, vol. 50, no. 1, pp. 53-77.
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Pan, S, Wu, J, Zhu, X, Long, G & Zhang, C 2017, 'Task Sensitive Feature Exploration and Learning for Multitask Graph Classification', IEEE Transactions on Cybernetics, vol. 47, no. 3, pp. 744-758.
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Wang, H, Wu, J, Pan, S, Zhang, P & Chen, L 2017, 'Towards large-scale social networks with online diffusion provenance detection', Computer Networks, vol. 114, pp. 154-166.
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Wu, J, Pan, S, Zhu, X, Zhang, C & Wu, X 2017, 'Positive and Unlabeled Multi-Graph Learning', IEEE Transactions on Cybernetics, vol. 47, no. 4, pp. 818-829.
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Wu, J, Pan, S, Zhu, X, Zhang, C & Yu, PS 2017, 'Multiple Structure-View Learning for Graph Classification', IEEE Transactions on Neural Networks and Learning Systems, vol. 29, no. 7, pp. 1-16.
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Zhang, Q, Wu, J, ZHANG, P, Long, G & Zhang, C 2017, 'Collective Hyping Detection System for Identifying Online Spam Activities', IEEE Intelligent Systems, vol. 32, no. 5, pp. 1-1.
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Zhang, Y, Wu, J, Zhou, C & Cai, Z 2017, 'Instance cloned extreme learning machine', Pattern Recognition, vol. 68, pp. 52-65.
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Wu, J, Hong, Z, Pan, S, Zhu, X, Cai, Z & Zhang, C 2016, 'Multi-graph-view subgraph mining for graph classification', Knowledge and Information Systems, vol. 48, no. 1, pp. 29-54.
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Wu, J, Pan, S, Zhu, X, Zhang, P & Zhang, C 2016, 'SODE: Self-Adaptive One-Dependence Estimators for classification', PATTERN RECOGNITION, vol. 51, pp. 358-377.
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Zhang, P, He, J, Long, G, Huang, G & Zhang, C 2016, 'Towards Anomalous Diffusion Sources Detection in a Large Network', ACM Transactions on Internet Technology, vol. 16, no. 1, pp. 1-24.
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Zhang, Q, Zhang, P, Long, G, Ding, W, Zhang, C & Wu, X 2016, 'Online Learning from Trapezoidal Data Streams', IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 10, pp. 2709-2723.
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Pan, S, Wu, J, Zhu, X, Zhang, C & Wang, Y 1970, 'Tri-party deep network representation', IJCAI International Joint Conference on Artificial Intelligence, International Joint Conference on Artificial Intelligence, AAAI Press / International Joint Conferences on Artificial Intelligence, New York City, New York, United States, pp. 1895-1901.

Ruiqi Hu, Pan, S, Guodong Long, Xingquan Zhu, Jing Jiang & Chengqi Zhang 1970, 'Co-clustering enterprise social networks', 2016 International Joint Conference on Neural Networks (IJCNN), 2016 International Joint Conference on Neural Networks (IJCNN), IEEE, IEEE, pp. 107-114.
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Wu, W, Li, B, Chen, L & Zhang, C 1970, 'Cross-View Feature Hashing for Image Retrieval', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Auckland, New Zealand, pp. 203-214.
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Zhang, D, Yin, J, Zhu, X & Zhang, C 1970, 'Homophily, Structure, and Content Augmented Network Representation Learning', 2016 IEEE 16th International Conference on Data Mining (ICDM), 2016 IEEE 16th International Conference on Data Mining (ICDM), IEEE, Barcelona, Spain, pp. 609-618.
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Zhang, Q, Wu, J, Yang, H, Lu, W, Long, G & Zhang, C 1970, 'Global and Local Influence-based Social Recommendation', Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, CIKM'16: ACM Conference on Information and Knowledge Management, ACM, Indianapolis, USA, pp. 1917-1920.
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Zhang, Q, Zhang, Q, Long, G, Zhang, P & Zhang, C 1970, 'Exploring Heterogeneous Product Networks for Discovering Collective Marketing Hyping Behavior', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer International Publishing, Auckland, New Zealand, pp. 40-51.
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Bin Li, Xingquan Zhu, Ruijiang Li & Chengqi Zhang 2015, 'Rating Knowledge Sharing in Cross-Domain Collaborative Filtering', IEEE Transactions on Cybernetics, vol. 45, no. 5, pp. 1068-1082.
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Pan, S, Wu, J, Zhu, X, Long, G & Zhang, C 2015, 'Finding the best not the most: regularized loss minimization subgraph selection for graph classification', PATTERN RECOGNITION, vol. 48, no. 11, pp. 3783-3796.
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Haishuai Wang, Zhang, P, Ling Chen, Huan Liu & Chengqi Zhang 1970, 'Online diffusion source detection in social networks', 2015 International Joint Conference on Neural Networks (IJCNN), 2015 International Joint Conference on Neural Networks (IJCNN), IEEE, Killarney, Ireland, pp. 1-8.
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Wang, H, Zhang, P, Chen, L & Zhang, C 1970, 'SocialAnalysis: A Real-Time Query and Mining System from Social Media Data Streams', Databases Theory and Applications (LNCS), Australasian Database Conference, Springer International Publishing, Melbourne, Australia, pp. 318-322.
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Wang, H, Zhang, P, Tsang, I, Chen, L & Zhang, C 1970, 'Defragging Subgraph Features for Graph Classification', Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM'15: 24th ACM International Conference on Information and Knowledge Management, ACM, Melbourne, VIC, Australia, pp. 1687-1690.
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Zhang, Q, Zhang, P, Long, G, Ding, W, Zhang, C & Wu, X 1970, 'Towards Mining Trapezoidal Data Streams', 2015 IEEE International Conference on Data Mining, 2015 IEEE International Conference on Data Mining (ICDM), IEEE, Atlantic City, New Jersey, United States, pp. 1111-1116.
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Wu, J, Hong, Z, Pan, S, Zhu, X, Cai, Z & Zhang, C 1970, 'Multi-graph-view Learning for Graph Classification', 2014 IEEE International Conference on Data Mining, 2014 IEEE International Conference on Data Mining (ICDM), IEEE, Buenos Aires, pp. 590-599.
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Keywords: Data mining,Social network analysis,Big information network search

FOR Codes: Pattern Recognition and Data Mining, Application Software Packages (excl. Computer Games), Database Management, Information Processing Services (incl. Data Entry and Capture), Information Systems Development Methodologies, Application Tools and System Utilities, Information systems development methodologies and practice, Knowledge representation and reasoning, Database systems, Application software packages, Information systems, technologies and services not elsewhere classified

Acknowledgement of Country

UTS acknowledges the Gadigal people of the Eora Nation, the Boorooberongal people of the Dharug Nation, the Bidiagal people and the Gamaygal people, upon whose ancestral lands our university stands. We would also like to pay respect to the Elders both past and present, acknowledging them as the traditional custodians of knowledge for these lands.