103 citations · 360 across the 124 of their papers we have counts for
8 papers · 1 filter
Efficient Maximum Fair Clique Search over Large Networks
Qi Zhang, Rong-Hua Li, Zifan Zheng +3
Mining cohesive subgraphs in attributed graphs is an essential problem in the domain of graph data analysis. The integration of fairness considerations significantly fuels interest…
Breaking the Entanglement of Homophily and Heterophily in Semi-supervised Node Classification
Henan Sun, Xunkai Li, Zhengyu Wu +3
Recently, graph neural networks (GNNs) have shown prominent performance in semi-supervised node classification by leveraging knowledge from the graph database. However, most existi…
Scalable Algorithms for Laplacian Pseudo-inverse Computation
Meihao Liao, Rong-Hua Li, Qiangqiang Dai +2
The pseudo-inverse of a graph Laplacian matrix, denoted as , finds extensive application in various graph analysis tasks. Notable examples include the calculation of ele…
Learning to Generate Parameters of ConvNets for Unseen Image Data
Shiye Wang, Kaituo Feng, Changsheng Li +2
Typical Convolutional Neural Networks (ConvNets) depend heavily on large amounts of image data and resort to an iterative optimization algorithm (e.g., SGD or Adam) to learn networ…
Privacy-Preserving Graph Embedding based on Local Differential Privacy
Zening Li, Rong-Hua Li, Meihao Liao +2
Graph embedding has become a powerful tool for learning latent representations of nodes in a graph. Despite its superior performance in various graph-based machine learning tasks,…
DREAM: Domain-free Reverse Engineering Attributes of Black-box Model
Rongqing Li, Jiaqi Yu, Changsheng Li +3
Deep learning models are usually black boxes when deployed on machine learning platforms. Prior works have shown that the attributes (, the number of convolutional layers) of…
Shared Growth of Graph Neural Networks via Prompted Free-direction Knowledge Distillation
Kaituo Feng, Yikun Miao, Changsheng Li +2
Knowledge distillation (KD) has shown to be effective to boost the performance of graph neural networks (GNNs), where the typical objective is to distill knowledge from a deeper te…
Fairness-aware Maximal Biclique Enumeration on Bipartite Graphs
Ziqi Yin, Qi Zhang, Wentao Zhang +2
Maximal biclique enumeration is a fundamental problem in bipartite graph data analysis. Existing biclique enumeration methods mainly focus on non-attributed bipartite graphs and al…