6 papers
Pruning Graphs by Adversarial Robustness Evaluation to Strengthen GNN Defenses
Yongyu Wang
Graph Neural Networks (GNNs) have emerged as a dominant paradigm for learning on graph-structured data, thanks to their ability to jointly exploit node features and relational info…
Enabling DBSCAN for Very Large-Scale High-Dimensional Spaces
Yongyu Wang
DBSCAN is one of the most important non-parametric unsupervised data analysis tools. By applying DBSCAN to a dataset, two key analytical results can be obtained: (1) clustering dat…
Defending Collaborative Filtering Recommenders via Adversarial Robustness Based Edge Reweighting
Yongyu Wang
User based collaborative filtering (CF) relies on a user and user similarity graph, making it vulnerable to profile injection (shilling) attacks that manipulate neighborhood relati…
Adversarial-Robustness-Guided Graph Pruning
Yongyu Wang
Graph learning plays a central role in many data mining and machine learning tasks, such as manifold learning, data representation and analysis, dimensionality reduction, clusterin…
Accelerate 3D Object Processing via Spectral Layout
Yongyu Wang
3D image processing is an important problem in computer vision and pattern recognition fields. Compared with 2D image processing, its computation difficulty and cost are much highe…
GRASPEL: Graph Spectral Learning at Scale
Yongyu Wang, Zhiqiang Zhao, Zhuo Feng
Learning meaningful graphs from data plays important roles in many data mining and machine learning tasks, such as data representation and analysis, dimension reduction, data clust…