93 citations · 181 across the 17 of their papers we have counts for
7 papers · 1 filter
PSNE: Efficient Spectral Sparsification Algorithms for Scaling Network Embedding
Longlong Lin, Yunfeng Yu, Zihao Wang +4
Network embedding has numerous practical applications and has received extensive attention in graph learning, which aims at mapping vertices into a low-dimensional and continuous d…
Edge Classification on Graphs: New Directions in Topological Imbalance
Xueqi Cheng, Yu Wang, Yunchao Liu +3
Recent years have witnessed the remarkable success of applying Graph machine learning (GML) to node/graph classification and link prediction. However, edge classification task that…
A Topological Perspective on Demystifying GNN-Based Link Prediction Performance
Yu Wang, Tong Zhao, Yuying Zhao +4
Graph Neural Networks (GNNs) have shown great promise in learning node embeddings for link prediction (LP). While numerous studies aim to improve the overall LP performance of GNNs…
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications
Yi Zhang, Yuying Zhao, Zhaoqing Li +5
Graph Neural Networks (GNNs) have gained significant attention owing to their ability to handle graph-structured data and the improvement in practical applications. However, many o…
Fairness and Explainability: Bridging the Gap Towards Fair Model Explanations
Yuying Zhao, Yu Wang, Tyler Derr
While machine learning models have achieved unprecedented success in real-world applications, they might make biased/unfair decisions for specific demographic groups and hence resu…
Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage
Yu Wang, Yuying Zhao, Yushun Dong +3
Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discr…
Imbalanced Graph Classification via Graph-of-Graph Neural Networks
Yu Wang, Yuying Zhao, Neil Shah +1
Graph Neural Networks (GNNs) have achieved unprecedented success in identifying categorical labels of graphs. However, most existing graph classification problems with GNNs follow…