4 papers
A Latent Logistic Regression Model with Graph Data
Haixiang Zhang, Yingjun Deng, Alan J. X. Guo +2
Recently, graph (network) data is an emerging research area in artificial intelligence, machine learning and statistics. In this work, we are interested in whether node's labels (p…
Tackling the Imbalance for GNNs
Rui Wang, Weixuan Xiong, Qinghu Hou +1
Different from deep neural networks for non-graph data classification, graph neural networks (GNNs) leverage the information exchange between nodes (or samples) when representing n…
A Mathematical Foundation for Robust Machine Learning based on Bias-Variance Trade-off
Ou Wu, Weiyao Zhu, Yingjun Deng +2
A common assumption in machine learning is that samples are independently and identically distributed (i.i.d). However, the contributions of different samples are not identical in…
Improving the Expressive Power of Graph Neural Network with Tinhofer Algorithm
Alan J. X. Guo, Qing-Hu Hou, Ou Wu
In recent years, Graph Neural Network (GNN) has bloomly progressed for its power in processing graph-based data. Most GNNs follow a message passing scheme, and their expressive pow…