Abstract: Inspired by attention mechanism and transductive learning method, we proposed a node classification algorithm based on weighted meta-learning. Firstly, Euclidean distance was used to calculate the difference of data distribution between subtasks in meta-learning. Secondly, adjacency matrices of subgraph was used  to calculate and capture structural difference of data points  between subtasks. Finally, the captured information above between subtasks were converted into weights  to weight the process of updating the  meta-learner in the meta-training procedure, and  an optimized meta-learning model was constructed to solve the problem that the loss of all meta-training subtasks in meta-training procedure of classical meta-learning algorithms was equal-weight to update the parameters of meta-learners. The experimental results of this algorithm on Citeseer and Cora datasets are superior to other classical algorithms, which demonstrates the effectiveness of the algorithm on few-shot node classification task.

Key words: meta-learning, attention mechanism, node classification, transductive learning