关键词: 网络表示学习, 属性网络, 深度学习

Abstract:

Attributed network representation learning aims to combine structure information and attribute information to learn a uniform vector representation for the nodes in the network. The existing method cannot enhance its complementary structural information enough when learning attribute information, thus affecting the final representation. In order to improve the representation quality, this paper proposes a structure-enhanced attributed network representation learning method. More specifically, this method extracts attribute information that enhances the global adjacency of the network through an autoencoder based on the normalized adjacency matrix and attribute matrix, the skip-gram model is used to capture local structure information, and a joint loss function is introduced to make the structure information and attribute information represent in the same vector space. Extensive experiments of node classification and link prediction on three real attribute networks demonstrate a superior performance of the proposed method over state-of-the-art for network representation learning.

Key words: network representation learning, attributed network, deep learning