关键词: 属性网络, 半监督学习, 表示学习, 聚类

Abstract: Network representation learning is an important research topic, which purpose is to represent high-dimensional attribute networks as low-dimensional dense vectors, so as to provide effective feature representation for the next task. SNE, a recently proposed attribute network representation learning model, uses both network structure and attribute information to represent learning network nodes. However, this model belongs to the unsupervised model and cannot make full use of some easily acquired prior information to improve the quality of the learned feature representation. Based on the above considerations, a semi-supervised attribute network representation learning method SSNE is proposed, which takes the attribute network and a small number of node priors as input of the feedforward neural network, learning the optimal node representation by maintaining the network link structure and a small number of node priors in the output layer through multiple hidden layer nonlinear transformations. Compared with the existing mainstream methods on four real attribute networks and two artificial attribute networks, the results show that the representation learned in this method has better performance in clustering and classification tasks.

Key words: attribute networks, semi-supervised learning, representation learning, clustering