14 citations · 39 across the 18 of their papers we have counts for
18 papers
Spectral structural distortion reveals redundant neurons in neural networks
Yongyu Wang
Overparameterized neural networks often contain many removable neurons, yet what makes a neuron redundant remains poorly understood. Existing pruning criteria commonly rely on loca…
T2T-LA: A Topology-to-Topology LLM Agent for Graph Learning with Neither Feature Access nor Task Knowledge
Yongyu Wang
Graph learning aims to convert data into graph representations, which are fundamental to many problems in machine learning for CAD, where circuits, layouts, designs, and optimizati…
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…
Spectrally unstable nodes drive reliability failures in graph learning
Yongyu Wang
Graph-learning algorithms can fail when graph structure is adversarially perturbed, intrinsically noisy or constructed from imperfect observations. Here we show that some nodes bea…
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…
Mitigating the Impact of Noisy Edges on Graph-Based Algorithms via Adversarial Robustness Evaluation
Yongyu Wang, Xiaotian Zhuang
Given that no existing graph construction method can generate a perfect graph for a given dataset, graph-based algorithms are often affected by redundant and erroneous edges presen…
Improving Collaborative Filtering Recommendation via Graph Learning
Yongyu Wang
Recommendation systems aim to provide personalized predictions by identifying items that are most appealing to individual users. Among various recommendation approaches, k-nearest-…
Towards High-Performance Exploratory Data Analysis (EDA) Via Stable Equilibrium Point
Yuxuan Song, Yongyu Wang
Exploratory data analysis (EDA) is a vital procedure for data science projects. In this work, we introduce a stable equilibrium point (SEP) - based framework for improving the effi…
Accelerate Support Vector Clustering via Spectrum-Preserving Data Compression
Yuxuan Song, Yongyu Wang
This paper proposes a novel framework for accelerating support vector clustering. The proposed method first computes much smaller compressed data sets while preserving the key clus…
Enhancing Graph Topology and Clustering Quality: A Modularity-Guided Approach
Yongyu Wang, Shiqi Hao, Xiaoyang Wang +1
Current modularity-based community detection algorithms attempt to find cluster memberships that maximize modularity within a fixed graph topology. Diverging from this conventional…
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…
Improving Spectral Clustering Using Spectrum-Preserving Node Aggregation
Yongyu Wang
Spectral clustering is one of the most popular clustering methods. However, the high computational cost due to the involved eigen-decomposition procedure can immediately hinder its…
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…
GraphZoom: A multi-level spectral approach for accurate and scalable graph embedding
Chenhui Deng, Zhiqiang Zhao, Yongyu Wang +2
Graph embedding techniques have been increasingly deployed in a multitude of different applications that involve learning on non-Euclidean data. However, existing graph embedding m…
Nearly-Linear Time Spectral Graph Reduction for Scalable Graph Partitioning and Data Visualization
Zhiqiang Zhao, Yongyu Wang, Zhuo Feng
This paper proposes a scalable algorithmic framework for spectral reduction of large undirected graphs. The proposed method allows computing much smaller graphs while preserving th…
Towards Scalable Spectral Clustering via Spectrum-Preserving Sparsification
Yongyu Wang, Zhuo Feng
The eigendeomposition of nearest-neighbor (NN) graph Laplacian matrices is the main computational bottleneck in spectral clustering. In this work, we introduce a highly-scalable, s…