1 citations · 2 across the 3 of their papers we have counts for
3 papers
math.SP2024★ 1 cited
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…
math.OC2023
Federated Primal Dual Fixed Point Algorithm
Ya-Nan Zhu, Jingwei Liang, Xiaoqun Zhang
Federated learning (FL) is a distributed learning paradigm that allows several clients to learn a global model without sharing their private data. In this paper, we generalize a pr…
cs.LG2022★ 1 cited
Robust Graph Representation Learning for Local Corruption Recovery
Bingxin Zhou, Yuanhong Jiang, Yu Guang Wang +4
The performance of graph representation learning is affected by the quality of graph input. While existing research usually pursues a globally smoothed graph embedding, we believe…