37 citations · 80 across the 10 of their papers we have counts for
10 papers
Globally Interpretable Graph Learning via Distribution Matching
Yi Nian, Yurui Chang, Wei Jin +1
Graph neural networks (GNNs) have emerged as a powerful model to capture critical graph patterns. Instead of treating them as black boxes in an end-to-end fashion, attempts are ari…
Renewable Composite Quantile Method and Algorithm for Nonparametric Models with Streaming Data
Yan Chen, Shuixin Fang, Lu Lin
We are interested in renewable estimations and algorithms for nonparametric models with streaming data. In our method, the nonparametric function of interest is expressed through a…
Spectral Augmentation for Self-Supervised Learning on Graphs
Lu Lin, Jinghui Chen, Hongning Wang
Graph contrastive learning (GCL), as an emerging self-supervised learning technique on graphs, aims to learn representations via instance discrimination. Its performance heavily re…
Feature screening for multi-response linear models by empirical likelihood
Jun Lu, Qinqin Hu, Lu Lin
This paper proposes a new feature screening method for the multi-response ultrahigh dimensional linear model by empirical likelihood. Through a multivariate moment condition, the e…
Graph Structural Attack by Perturbing Spectral Distance
Lu Lin, Ethan Blaser, Hongning Wang
Graph Convolutional Networks (GCNs) have fueled a surge of research interest due to their encouraging performance on graph learning tasks, but they are also shown vulnerability to…
Graph Embedding with Hierarchical Attentive Membership
Lu Lin, Ethan Blaser, Hongning Wang
The exploitation of graph structures is the key to effectively learning representations of nodes that preserve useful information in graphs. A remarkable property of graph is that…