31 citations · 34 across the 6 of their papers we have counts for
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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★ 31 cited
Zero-Knowledge Proof-based Verifiable Decentralized Machine Learning in Communication Network: A Comprehensive Survey
Zhibo Xing, Zijian Zhang, Ziang Zhang +8
Over recent decades, machine learning has significantly advanced network communication, enabling improved decision-making, user behavior analysis, and fault detection. Decentralize…
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…