2 citations · 2 across the 3 of their papers we have counts for
3 papers
cs.LG2024★ 2 cited
DropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
Zeyu Zhang, Lu Li, Shuyan Wan +5
The paper discusses signed graphs, which model friendly or antagonistic relationships using edges marked with positive or negative signs, focusing on the task of link sign predicti…
cs.LG2023
SGA: A Graph Augmentation Method for Signed Graph Neural Networks
Zeyu Zhang, Shuyan Wan, Sijie Wang +5
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links. However, three key challenges hind…
cs.LG2023
Enhancing Signed Graph Neural Networks through Curriculum-Based Training
Zeyu Zhang, Lu Li, Xingyu Ji +5
Signed graphs are powerful models for representing complex relations with both positive and negative connections. Recently, Signed Graph Neural Networks (SGNNs) have emerged as pot…