Rich heterogeneous information preserving network representation learning

Network representation learning has attracted increasing attention recently due to its applicability in network analysis. However, most existing network representation learning models only focus on preserving fragmentary aspects of network information, either node proximities or fixed semantic information. In this paper, we propose a novel algorithm named Rich Heterogeneous Information Preserving Network Representation Learning (HIRL), which integrates the high-order proximity among nodes and semantic information into a generic framework by exploiting a flexible autoencoder network. Based on the proposed algorithm, we can explore the hidden information in heterogeneous information networks through any custom form of path schema, and represents different types of nodes in a continuous and common vector space. Moreover, the proposed HIRL is applicable to homogeneous information networks. Extensive experimental results demonstrate that our approach can effectively preserve the information in networks under various path schemas, and performs better on real-world applications such as network reconstruction, link prediction, and node classification compared with the state-of-the-art methods. (c) 2020 Elsevier Ltd. All rights reserved.