337 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Chinese Academy of SciencesCN7 papers
- Beijing Normal UniversityCN4 papers
- Central University of Finance and EconomicsCN4 papers
- Peking UniversityCN4 papers
- Renmin University of ChinaCN4 papers
- Tohoku UniversityJP4 papers
- Beihang UniversityCN3 papers
- The University of Texas at San AntonioUS3 papers
- Academy of Mathematics and Systems ScienceCN2 papers
- Beijing University of Chemical TechnologyCN2 papers
- Centre National de la Recherche ScientifiqueFR2 papers
- Hong Kong University of Science and TechnologyHK2 papers
5 papers · 1 filter
Rank and Align: Towards Effective Source-free Graph Domain Adaptation
Junyu Luo, Zhiping Xiao, Yifan Wang +5
Graph neural networks (GNNs) have achieved impressive performance in graph domain adaptation. However, extensive source graphs could be unavailable in real-world scenarios due to p…
R-Mixup: Riemannian Mixup for Biological Networks
Xuan Kan, Zimu Li, Hejie Cui +6
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological ent…
A Comprehensive Survey on Deep Graph Representation Learning
Wei Ju, Zheng Fang, Yiyang Gu +13
Graph representation learning aims to effectively encode high-dimensional sparse graph-structured data into low-dimensional dense vectors, which is a fundamental task that has been…
AWCD: An Efficient Point Cloud Processing Approach via Wasserstein Curvature
Yihao Luo, Ailing Yang, Fupeng Sun +1
In this paper, we introduce the adaptive Wasserstein curvature denoising (AWCD), an original processing approach for point cloud data. By collecting curvatures information from Was…
Learning from Label Proportions with Generative Adversarial Networks
Jiabin Liu, Bo Wang, Zhiquan Qi +2
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level propo…