CSG: Curriculum Representation Learning for Signed Graph

no code implementations • 17 Oct 2023 • Zeyu Zhang, Jiamou Liu, Kaiqi Zhao, Yifei Wang, Pengqian Han, Xianda Zheng, Qiqi Wang, Zijian Zhang

Signed graphs are valuable for modeling complex relationships with positive and negative connections, and Signed Graph Neural Networks (SGNNs) have become crucial tools for their analysis.

Link Sign Prediction Representation Learning

SGA: A Graph Augmentation Method for Signed Graph Neural Networks

no code implementations • 15 Oct 2023 • Zeyu Zhang, Shuyan Wan, Sijie Wang, Xianda Zheng, Xinrui Zhang, Kaiqi Zhao, Jiamou Liu, Dong Hao

Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links.

Data Augmentation Graph Representation Learning +1

Enhancing Student Performance Prediction on Learnersourced Questions with SGNN-LLM Synergy

no code implementations • 23 Sep 2023 • Lin Ni, Sijie Wang, Zeyu Zhang, Xiaoxuan Li, Xianda Zheng, Paul Denny, Jiamou Liu

Learnersourcing offers great potential for scalable education through student content creation.

Contrastive Learning Language Modelling +1