关键词: 网络表示学习, 随机游走, Motif结构

Abstract: With the widespread use of information technologies, networks are becoming ubiquitous in real-word applications. Network representation learning (also called network embedding) has been attracting increasing attention in the recent years, which aims to learn latent, low-dimensional representations of network vertices, while preserving network topological structure. Network Motif structure is of key importance in network analysis. Most of the earlier works related to network representation learning only consider the node's neighborhood attributes or proximity, while ignoring the Motif information of nodes. Therefore, based on the above consideration, this paper proposes the Motif-preserving network embedding (MPNE) algorithm, which focuses more on Motif structure when learning vertex representations. The proposed algorithm firstly constructs a weighted network based on the given Motif structure and computes the approximate personalized PageRank vector (APPR) for the weighted network subsequently. Finally, the algorithm performs MotifWalk on the graph to get the path so that the Word2Vec model can be applied to obtain the vector representation of the network. Compared with three classical network representation algorithms, the experimental results show that the proposed algorithm is better in dense and Motif-rich networks.

Key words: network representation learning, random walk, Motif