3 citations · 6 across the 8 of their papers we have counts for
8 papers
Fast and Robust Contextual Node Representation Learning over Dynamic Graphs
Xingzhi Guo, Silong Wang, Baojian Zhou +2
Real-world graphs grow rapidly with edge and vertex insertions over time, motivating the problem of efficiently maintaining robust node representation over evolving graphs. Recent…
Sensiverse: A dataset for ISAC study
Jiajin Luo, Baojian Zhou, Yang Yu +6
In order to address the lack of applicable channel models for ISAC research and evaluation, we release Sensiverse, a dataset that can be used for ISAC research. In this paper, we p…
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…
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…