The paper ‘A Teacher-Free Graph Knowledge Distillation Framework with Dual Self-Distillation’ revolutionizes graph-related tasks by introducing a teacher-free framework for knowledge distillation.

• Proposes the Teacher-Free Graph Self-Distillation (TGS) framework, excluding the need for a teacher model or GNNs.
• Framework improves over vanilla MLPs by significant margins and surpasses existing GKD algorithms.
• Showcases the efficient influence of graph topology in training, without the drag of data dependency in inference.

Opinion: This innovative approach to graph knowledge distillation could change the way machine learning models interact with and benefit from graph data, offering a more efficient pathway to harnessing the power of neural networks in various industries.