关键词: 网络表示学习, 异质信息网络, 网络分析

Abstract: Network representation learning aims to learn a series of low-dimensional vectors for the components (node, edge, subgraph, etc.) in a network. Meanwhile, the characters of the components in the original network should be largely retained in these vectors. Heterogeneous information network is the network composed of various types of nodes, link relationships and attribute information. It is characterized by dynamics, large scale and heterogeneity, and is ubiquitous in the real life. Network representation learning by integrating various heterogeneous information can not only alleviate the problem of data sparsity, but also help to learn the representation vectors with high discriminative and inferential ability. At the same time, it also faces the challenge of dealing with complex data relationships and balancing heterogeneous information. In recent years, researchers have designed different representation learning algorithms for heterogeneous information networks, which have greatly promoted the development of this field. In view of these algorithms, this paper first designs a unified classification framework, then generalizes and compares the representative algorithms in each category, including their time complexities, advantages, etc. In addition, the information of the commonly used data sets is summarized into a table. Some challenges and possible research directions are provided at the end of this paper.

Key words: network representation learning, heterogeneous information network, network analysis