22 citations · 41 across the 16 of their papers we have counts for
16 papers
Accelerating Personalized PageRank Vector Computation
Zhen Chen, Xingzhi Guo, Baojian Zhou +2
Personalized PageRank Vectors are widely used as fundamental graph-learning tools for detecting anomalous spammers, learning graph embeddings, and training graph neural networks. T…
Does it pay to optimize AUC?
Baojian Zhou, Steven Skiena
The Area Under the ROC Curve (AUC) is an important model metric for evaluating binary classifiers, and many algorithms have been proposed to optimize AUC approximately. It raises t…
Fast Online Node Labeling for Very Large Graphs
Baojian Zhou, Yifan Sun, Reza Babanezhad
This paper studies the online node classification problem under a transductive learning setting. Current methods either invert a graph kernel matrix with runtime…
Subset Node Anomaly Tracking over Large Dynamic Graphs
Xingzhi Guo, Baojian Zhou, Steven Skiena
Tracking a targeted subset of nodes in an evolving graph is important for many real-world applications. Existing methods typically focus on identifying anomalous edges or finding a…
Approximate Frank-Wolfe Algorithms over Graph-structured Support Sets
Baojian Zhou, Yifan Sun
In this paper, we consider approximate Frank-Wolfe (FW) algorithms to solve convex optimization problems over graph-structured support sets where the linear minimization oracle (LM…
Subset Node Representation Learning over Large Dynamic Graphs
Xingzhi Guo, Baojian Zhou, Steven Skiena
Dynamic graph representation learning is a task to learn node embeddings over dynamic networks, and has many important applications, including knowledge graphs, citation networks t…
Stochastic Hard Thresholding Algorithms for AUC Maximization
Zhenhuan Yang, Baojian Zhou, Yunwen Lei +1
In this paper, we aim to develop stochastic hard thresholding algorithms for the important problem of AUC maximization in imbalanced classification. The main challenge is the pairw…
Online AUC Optimization for Sparse High-Dimensional Datasets
Baojian Zhou, Yiming Ying, Steven Skiena
The Area Under the ROC Curve (AUC) is a widely used performance measure for imbalanced classification arising from many application domains where high-dimensional sparse data is ab…
Dual Averaging Method for Online Graph-structured Sparsity
Baojian Zhou, Feng Chen, Yiming Ying
Online learning algorithms update models via one sample per iteration, thus efficient to process large-scale datasets and useful to detect malicious events for social benefits, suc…
Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
Baojian Zhou, Feng Chen, Yiming Ying
Stochastic optimization algorithms update models with cheap per-iteration costs sequentially, which makes them amenable for large-scale data analysis. Such algorithms have been wid…
A Generic Framework for Interesting Subspace Cluster Detection in Multi-attributed Networks
Feng Chen, Baojian Zhou, Adil Alim +1
Detection of interesting (e.g., coherent or anomalous) clusters has been studied extensively on plain or univariate networks, with various applications. Recently, algorithms have b…
Technical Report: A Generalized Matching Pursuit Approach for Graph-Structured Sparsity
Feng Chen, Baojian Zhou
Sparsity-constrained optimization is an important and challenging problem that has wide applicability in data mining, machine learning, and statistics. In this paper, we focus on s…
Can Self-Censorship in News Media be Detected Algorithmically? A Case Study in Latin America
Rongrong Tao, Baojian Zhou, Feng Chen +4
Censorship in social media has been well studied and provides insight into how governments stifle freedom of expression online. Comparatively less (or no) attention has been paid t…
Technical Report: Graph-Structured Sparse Optimization for Connected Subgraph Detection
Baojian Zhou, Feng Chen
Structured sparse optimization is an important and challenging problem for analyzing high-dimensional data in a variety of applications such as bioinformatics, medical imaging, soc…
A Quadratic Programming Relaxation Approach to Compute-and-Forward Network Coding Design
Baojian Zhou, Jinming Wen, Wai Ho Mow
Using physical layer network coding, compute-and-forward is a promising relaying scheme that effectively exploits the interference between users and thus achieves high rates. In th…
An Efficient Algorithm for Optimally Solving a Shortest Vector Problem in Compute-and-Forward Protocol Design
Jinming Wen, Baojian Zhou, Wai Ho Mow +1
We consider the problem of finding the optimal coefficient vector that maximizes the computation rate at a relay in the compute-and-forward scheme. Based on the idea of sphere deco…