44 citations · 60 across the 4 of their papers we have counts for
5 papers
G-Mixup: Graph Data Augmentation for Graph Classification
Xiaotian Han, Zhimeng Jiang, Ninghao Liu +1
This work develops \emph{mixup for graph data}. Mixup has shown superiority in improving the generalization and robustness of neural networks by interpolating features and labels b…
Geometric Graph Representation Learning via Maximizing Rate Reduction
Xiaotian Han, Zhimeng Jiang, Ninghao Liu +3
Learning discriminative node representations benefits various downstream tasks in graph analysis such as community detection and node classification. Existing graph representation…
FMP: Toward Fair Graph Message Passing against Topology Bias
Zhimeng Jiang, Xiaotian Han, Chao Fan +4
Despite recent advances in achieving fair representations and predictions through regularization, adversarial debiasing, and contrastive learning in graph neural networks (GNNs), t…
AutoRec: An Automated Recommender System
Ting-Hsiang Wang, Qingquan Song, Xiaotian Han +3
Realistic recommender systems are often required to adapt to ever-changing data and tasks or to explore different models systematically. To address the need, we present AutoRec, an…
Deep Collaborative Filtering with Multi-Aspect Information in Heterogeneous Networks
Chuan Shi, Xiaotian Han, Li Song +4
Recently, recommender systems play a pivotal role in alleviating the problem of information overload. Latent factor models have been widely used for recommendation. Most existing l…