HetCAN: A Heterogeneous Graph Cascade Attention Network with Dual-Level Awareness

1 code implementation • 6 Nov 2023 • Zeyuan Zhao, Qingqing Ge, Anfeng Cheng, Yiding Liu, Xiang Li, Shuaiqiang Wang

However, different types of nodes in heterogeneous graphs have diverse features, it is also necessary to capture interactions among node features, namely the high-order information from feature-level aspect.

Attribute

Enhancing Graph Neural Networks with Structure-Based Prompt

no code implementations • 26 Oct 2023 • Qingqing Ge, Zeyuan Zhao, Yiding Liu, Anfeng Cheng, Xiang Li, Shuaiqiang Wang, Dawei Yin

Graph Neural Networks (GNNs) are powerful in learning semantics of graph data.

Contrastive Learning Graph Classification +1

Layout-aware Webpage Quality Assessment

no code implementations • 28 Jan 2023 • Anfeng Cheng, Yiding Liu, Weibin Li, Qian Dong, Shuaiqiang Wang, Zhengjie Huang, Shikun Feng, Zhicong Cheng, Dawei Yin

To assess webpage quality from complex DOM tree data, we propose a graph neural network (GNN) based method that extracts rich layout-aware information that implies webpage quality in an end-to-end manner.

Geometry Contrastive Learning on Heterogeneous Graphs

1 code implementation • 25 Jun 2022 • Shichao Zhu, Chuan Zhou, Anfeng Cheng, Shirui Pan, Shuaiqiang Wang, Dawei Yin, Bin Wang

Self-supervised learning (especially contrastive learning) methods on heterogeneous graphs can effectively get rid of the dependence on supervisory data.

Contrastive Learning Node Classification +3

Deep Active Learning for Anchor User Prediction

1 code implementation • 18 Jun 2019 • Anfeng Cheng, Chuan Zhou, Hong Yang, Jia Wu, Lei LI, Jianlong Tan, Li Guo

Due to the expensive costs of labeling anchor users for training prediction models, we consider in this paper the problem of minimizing the number of user pairs across multiple networks for labeling as to improve the accuracy of the prediction.

Active Learning