1 citations · 1 across the 2 of their papers we have counts for
4 papers
Limiting Over-Smoothing and Over-Squashing of Graph Message Passing by Deep Scattering Transforms
Yuanhong Jiang, Dongmian Zou, Xiaoqun Zhang +1
Graph neural networks (GNNs) have become pivotal tools for processing graph-structured data, leveraging the message passing scheme as their core mechanism. However, traditional GNN…
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…
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…