基于节点相似度的无监督属性图嵌入模型

属性图嵌入旨在将属性图中的节点表示为低维向量,并同时保留节点的拓扑信息和属性信息.属性图嵌入已经有一系列相关工作,然而它们大多数提出的是有监督或半监督的算法.在实际应用中,需要标记的节点数量多,导致这些属性图嵌入算法的难度大,且需要消耗巨大的人力物力.针对上述问题以无监督的视角重新分析,提出了一种无监督的属性图嵌入算法...

Unsupervised attributed graph embedding model based on node similarity

Attributed graph embedding aims to represent the nodes in an attributed graph into low-dimensional vectors while preserving the topology information and attribute information of the nodes. There are lots of works related to attributed graph embedding. However， most of algorithms proposed in them are supervised or semi-supervised. In practical applications， the number of nodes that need to be labeled is large， which makes these algorithms difficult and consume huge manpower and material resources. Above problems were reanalyzed from an unsupervised perspective， and an unsupervised attributed graph embedding algorithm was proposed. Firstly， the topology information and attribute information of the nodes were calculated respectively by using the existing non-attributed graph embedding algorithm and attributes of the attributed graph. Then， the embedding vector of the nodes was obtained by using Graph Convolutional Network （GCN）， and the difference between the embedding vector and the topology information and the difference between the embedding vector and attribute information were minimized. Finally， similar embeddings was obtained by the paired nodes with similar topological information and attribute information. Compared with Graph Auto-Encoder （GAE） method， the proposed method has the node classification accuracy improved by 1.2 percentage points and 2.4 percentage points on Cora and Citeseer datasets respectively. Experimental results show that the proposed method can effectively improve the quality of the generated embedding.