CSG: Curriculum Representation Learning for Signed Graph
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. However, prior to our work, no specific
training plan existed for SGNNs, and the conventional random sampling approach
did not address varying learning difficulties within the graph's structure. We
proposed a curriculum-based training approach, where samples progress from easy
to complex, inspired by human learning. To measure learning difficulty, we
introduced a lightweight mechanism and created the Curriculum representation
learning framework for Signed Graphs (CSG). This framework optimizes the order
in which samples are presented to the SGNN model. Empirical validation across
six real-world datasets showed impressive results, enhancing SGNN model
accuracy by up to 23.7% in link sign prediction (AUC) and significantly
improving stability with an up to 8.4 reduction in the standard deviation of
AUC scores.