2 citations · 4 across the 3 of their papers we have counts for
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cs.LG2024
CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
Yiru Pan, Xingyu Ji, Jiaqi You +5
Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms. The transcription and regulatio…
cs.LG2024★ 1 cited
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process
Xingyu Ji, Jiale Liu, Lu Li +2
Representation learning on text-attributed graphs (TAGs) has attracted significant interest due to its wide-ranging real-world applications, particularly through Graph Neural Netwo…
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…