Proximity-aware heterogeneous information network embedding

Network embedding, which aims to learn a high-quality low-dimensional representation for each node in a network, has attracted increasing attention recently. Heterogeneous information networks, with distinguishing types of nodes and relations, are one of the most significant networks. In the past years, heterogeneous information network embedding has been intensively studied. Most popular methods generate a set of node sequences, and feed them into an unsupervised feature learning model to obtain a low-dimensional vector for each node. However, the limitations of these approaches are that their generative node sequences neglect the different importances of diverse relations and they ignore the great value of proximity information which reveals whether two nodes are close or not in the network. To tackle these limitations, this paper presents a novel framework named Proximity-Aware Heterogeneous Information Network Embedding (PAHINE). The native information of a network is extracted from node sequences, which are generated by walking on a probability-sensitive metagraph. Afterwards, the extracted information is fed into deep neural networks to derive the desired embedding vectors. The experimental results on four different heterogeneous networks indicate that the proposed method is efficient and it outperforms the state-of-the-art heterogeneous networks embedding algorithms. (c) 2020 Elsevier B.V. All rights reserved.