Student GNN

Student Graph Neural Networks (GNNs) leverage knowledge distillation to improve the efficiency and performance of smaller, "student" GNNs by learning from larger, more powerful "teacher" GNNs. Research focuses on developing effective knowledge transfer methods, including techniques like aligning neural heat kernels to capture geometric information and adaptive knowledge distillation to selectively incorporate complementary knowledge from multiple teachers. This approach addresses challenges in training deep GNNs and enables the deployment of efficient, high-performing GNNs in resource-constrained environments, impacting various applications requiring graph-structured data analysis.