477 citations · 1.2k across the 14 of their papers we have counts for
7 papers · 1 filter
DisCo: Graph-Based Disentangled Contrastive Learning for Cold-Start Cross-Domain Recommendation
Hourun Li, Yifan Wang, Zhiping Xiao +4
Recommender systems are widely used in various real-world applications, but they often encounter the persistent challenge of the user cold-start problem. Cross-domain recommendatio…
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…
DisenSemi: Semi-supervised Graph Classification via Disentangled Representation Learning
Yifan Wang, Xiao Luo, Chong Chen +3
Graph classification is a critical task in numerous multimedia applications, where graphs are employed to represent diverse types of multimedia data, including images, videos, and…
Hypergraph-enhanced Dual Semi-supervised Graph Classification
Wei Ju, Zhengyang Mao, Siyu Yi +6
In this paper, we study semi-supervised graph classification, which aims at accurately predicting the categories of graphs in scenarios with limited labeled graphs and abundant unl…
Towards Graph Contrastive Learning: A Survey and Beyond
Wei Ju, Yifan Wang, Yifang Qin +10
In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to i…
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
Wei Ju, Siyu Yi, Yifan Wang +10
Graph-structured data exhibits universality and widespread applicability across diverse domains, such as social network analysis, biochemistry, financial fraud detection, and netwo…
A Survey of Data-Efficient Graph Learning
Wei Ju, Siyu Yi, Yifan Wang +4
Graph-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems. While graph neural network…