基于邻域相似的层次粒化的网络表示学习方法

The acquisition of structural features brings higher complexity to network representation learning. Based on the idea of hierarchy, an effective method is proposed to reduce the complexity of existing network representation learning methods. The network is gradually compressed into a coarse-grained representation space via node neighborhood similarity. And the coarse-grained feature representation is learned by the existing network representation learning methods. Finally, the learned coarse-grained features are gradually refined into the node representation of the original network using the graph convolution network model. Experimental results on several datasets show that the proposed method compresses the network efficiently and quickly, and the running time of the existing algorithms is greatly reduced. For the task of node classification and link prediction, the proposed method can greatly improve the performance of the original algorithm while the granularity level is low.