AttrHIN: Network Representation Learning Method for Heterogeneous Information Network

Network representation learning can map complex network to low dimensional vector space, capture the topological properties of network, and reduce the time complexity and space complexity of the algorithm. However, most of the existing network representation learning (NRL) methods are for homogeneous networks, while the real-world networks are usually heterogeneous, therefore, it is more practical to provide an intelligent insight into the evolution of heterogeneous networks. In this paper, we propose a novel heterogeneous network embedding method, called AttrHIN, which adopts weighted meta-path-based random walks strategy, and can make full use of the attribute information to capture the latent features. AttrHIN is suitable for the different types of nodes in heterogeneous networks. Extensive experimental results show that compared with the state-of-art algorithms, AttrHIN achieves better results in Macro-F1 and Micro-F1 for multi-class node classification and Link Prediction on several real-world network datasets.