  The rapid development of graph neural networks (GNNs) encourages the rising
of link prediction, achieving promising performance with various applications.
Unfortunately, through a comprehensive analysis, we surprisingly find that
current link predictors with dynamic negative samplers (DNSs) suffer from the
migration phenomenon between "easy" and "hard" samples, which goes against the
preference of DNS of choosing "hard" negatives, thus severely hindering
capability. Towards this end, we propose the MeBNS framework, serving as a
general plugin that can potentially improve current negative sampling based
link predictors. In particular, we elaborately devise a Meta-learning Supported
Teacher-student GNN (MST-GNN) that is not only built upon teacher-student
architecture for alleviating the migration between "easy" and "hard" samples
but also equipped with a meta learning based sample re-weighting module for
helping the student GNN distinguish "hard" samples in a fine-grained manner. To
effectively guide the learning of MST-GNN, we prepare a Structure enhanced
Training Data Generator (STD-Generator) and an Uncertainty based Meta Data
Collector (UMD-Collector) for supporting the teacher and student GNN,
respectively. Extensive experiments show that the MeBNS achieves remarkable
performance across six link prediction benchmark datasets.
