14 citations · 54 across the 12 of their papers we have counts for
12 papers
FROG: Fair Removal on Graphs
Ziheng Chen, Jiali Cheng, Hadi Amiri +5
With growing emphasis on privacy regulations, machine unlearning has become increasingly critical in real-world applications such as social networks and recommender systems, many o…
Layer-diverse Negative Sampling for Graph Neural Networks
Wei Duan, Jie Lu, Yu Guang Wang +1
Graph neural networks (GNNs) are a powerful solution for various structure learning applications due to their strong representation capabilities for graph data. However, traditiona…
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…
Knowledge Graph Prompting for Multi-Document Question Answering
Yu Wang, Nedim Lipka, Ryan A. Rossi +3
The `pre-train, prompt, predict' paradigm of large language models (LLMs) has achieved remarkable success in open-domain question answering (OD-QA). However, few works explore this…
Fairness-Aware Graph Neural Networks: A Survey
April Chen, Ryan A. Rossi, Namyong Park +6
Graph Neural Networks (GNNs) have become increasingly important due to their representational power and state-of-the-art predictive performance on many fundamental learning tasks.…
Fairness and Diversity in Recommender Systems: A Survey
Yuying Zhao, Yu Wang, Yunchao Liu +3
Recommender systems are effective tools for mitigating information overload and have seen extensive applications across various domains. However, the single focus on utility goals…
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…
ChemicalX: A Deep Learning Library for Drug Pair Scoring
Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva +9
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The prim…
Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification
Yu Wang, Charu Aggarwal, Tyler Derr
Recent years have witnessed the significant success of applying graph neural networks (GNNs) in learning effective node representations for classification. However, current GNNs ar…
Tree Decomposed Graph Neural Network
Yu Wang, Tyler Derr
Graph Neural Networks (GNNs) have achieved significant success in learning better representations by performing feature propagation and transformation iteratively to leverage neigh…
Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification
Yu Wang, Yuesong Shen, Daniel Cremers
Node features and structural information of a graph are both crucial for semi-supervised node classification problems. A variety of graph neural network (GNN) based approaches have…