30 citations · 32 across the 5 of their papers we have counts for
5 papers
HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness
Zeyuan Zhao, Qingqing Ge, Anfeng Cheng +3
Heterogeneous graph neural networks(HGNNs) have recently shown impressive capability in modeling heterogeneous graphs that are ubiquitous in real-world applications. Most existing…
PSP: Pre-Training and Structure Prompt Tuning for Graph Neural Networks
Qingqing Ge, Zeyuan Zhao, Yiding Liu +4
Graph Neural Networks (GNNs) are powerful in learning semantics of graph data. Recently, a new paradigm "pre-train and prompt" has shown promising results in adapting GNNs to vario…
Layout-aware Webpage Quality Assessment
Anfeng Cheng, Yiding Liu, Weibin Li +6
Identifying high-quality webpages is fundamental for real-world search engines, which can fulfil users' information need with the less cognitive burden. Early studies of \emph{webp…
Geometry Contrastive Learning on Heterogeneous Graphs
Shichao Zhu, Chuan Zhou, Anfeng Cheng +4
Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data. Meanwhile, most existing r…
Deep Active Learning for Anchor User Prediction
Anfeng Cheng, Chuan Zhou, Hong Yang +4
Predicting pairs of anchor users plays an important role in the cross-network analysis. Due to the expensive costs of labeling anchor users for training prediction models, we consi…