Qing WangAssociate Professor |
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I lead the Graph Research Lab and the Database Group at the School of Computing, ANU. My research broadly lies in the areas of databases, data mining, graph algorithms and machine learning. I am interested in understanding theoretical aspects of data management and analysis, and exploring their potential in supporting practitioners.
The Graph Research Lab and the Database Group are
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Fast Fully Dynamic Labelling For Distance Queries
M. Farhan, Q. Wang, Y. Lin and B. McKay
The VLDB Journal, 2021.
( Paper link ) |
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A Regularized Wasserstein Framework for Graph Kernels
A. Wijesinghe, Q. Wang and S. Gould
IEEE International Conference on Data Mining (ICDM), 2021.
( Paper link ) |
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Beyond Low-Pass Filters: Adaptive Feature Propagation on Graphs
S. Li, D. Kim and Q. Wang
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2021.
( Paper link ) |
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Efficient Maintenance of Distance Labelling for Incremental Updates in Large Dynamic Graphs
M. Farhan and Q. Wang
The 24th International Conference on Extending Database Technology (EDBT), 2021.
( Paper link | slides ) |
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Query-by-Sketch: Scaling Shortest Path Graph Queries on Very Large Networks
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Episode-Adaptive Embedding Networks for Few-Shot Learning
F. Liu and Q. Wang
The 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2021.
( Paper link ) |
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dK-Projection: Publishing Graph Joint Degree Distribution with Node Differential Privacy
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ErGAN: Generative Adversarial Networks for Entity Resolution
J. Shao, Q. Wang, A. Wijesinghe, and E. Rahm
IEEE International Conference on Data Mining (ICDM), 2020.
( Preprint | Extended Version ) |
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Dynamic Chunkwise CNN for Distantly Supervised Relation Extraction
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dK-Microaggregation: Anonymizing Graphs with Differential Privacy Guarantees
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DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters
A. Wijesinghe and Q. Wang
Thirty-third Annual Conference on Neural Information Processing Systems (NeurIPS), 2019.
( Preprint | Github Code | Poster ) |
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Learning to Sample: an Active Learning Framework
J. Shao, Q. Wang and F. Liu
IEEE International Conference on Data Mining (ICDM), 2019.
( Preprint ) |
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CoCoOn: Cloud Computing Ontology for IaaS Price and Performance Comparison
Q. Zhang, A. Haller and Q. Wang
The 18th International Semantic Web Conference (ISWC), 2019.
( Preprint ) |
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Skyblocking for Entity Resolution
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Knowledge Tracing with Sequential Key-Value Memory Networks
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A Highly Scalable Labelling Approach for Exact Distance Queries in Complex Networks
M. Farhan, Q. Wang, Y. Lin, and B. McKay
The 22nd International Conference on Extending Database Technology (EDBT), 2019.
( PDF | Preprint | Github Code ) |
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Publishing Differentially Private Datasets via Stable Microaggregation (short paper)
M. Iftikhar, Q. Wang, and Y. Lin
The 22nd International Conference on Extending Database Technology (EDBT), 2019.
( Preprint ) |
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Repairing of Record Linkage: Turing Errors into Insight (short paper)
Q. Bui-Nguyen, Q. Wang, J. Shao, and D. Vatsalan
The 22nd International Conference on Extending Database Technology (EDBT), 2019.
( Preprint ) |
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FACH: Fast Algorithm for Detecting Cohesive Hierarchies of Communities in Large Networks
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Attentive Graph-based Recursive Neural Network for Collective Vertex Classification
Q. Xu, Q. Wang, C. Xu and L. Qu
The 26th ACM International Conference on Information and Knowledge Management (CIKM), 2017.
( PDF ) |
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A Complete Logic for Database Abstract State Machines
F. Ferrarotti, K.-D. Schewe, L. Tec, and Q. Wang
Logic Journal of the IGPL, 2017.
( PDF ) |
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Flower: A Data Analytics Flow Elasticity Manager
A. Khoshkbarforoushha, R. Ranjan, Q. Wang and C. Friedrich
The 43rd International Conference on Very Large Data Bases (VLDB), demo paper, 2017.
( PDF ) |
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Improving Temporal Record Linkage using Regression Classification
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Temporal Group Linkage and Evolution Analysis for Census Data
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Semantic-Aware LSH Blocking for Entity Resolution
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Rogas: A Declarative Framework for Network Analytics
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Population Informatics using Big Data (tutorial)
P. Christen, H-C. Kum, Q. Wang and D. Vatsalan
The 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2016.
( Slides ) |
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A New Thesis concerning Synchronised Parallel Computing - Simplified Parallel ASM Thesis
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A Clustering-based Framework to Control Block Size for Entity Resolution
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Efficient Entity Resolution with Adaptive and Interactive Training Data Selection
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Efficient Interactive Training Selection for Large-scale Entity Resolution
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A Theoretical Framework for Knowledge-based Entity Resolution
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Network Analytics ER Model:Towards a Conceptual View of Network Analytics
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Data Migration: A Theoretical Perspective
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