AHINE: Adaptive Heterogeneous Information Network Embedding
This addresses the problem of generating effective embeddings for heterogeneous networks, which is important for applications like ride-hailing and bibliographic analysis, but it appears incremental as it builds on prior work with adaptive mechanisms.
The paper tackles the problem of network embedding for heterogeneous networks by proposing two algorithms, GHINE and AHINE, with AHINE using an adaptive deep model to preserve relationship chains between non-adjacent nodes. The result includes superior accuracy on a ride-hailing platform's prediction problems and outperforming state-of-the-art methods on public datasets for tasks like node labeling and similarity ranking.
Network embedding is an effective way to solve the network analytics problems such as node classification, link prediction, etc. It represents network elements using low dimensional vectors such that the graph structural information and properties are maximumly preserved. Many prior works focused on embeddings for networks with the same type of edges or vertices, while some works tried to generate embeddings for heterogeneous network using mechanisms like specially designed meta paths. In this paper, we propose two novel algorithms, GHINE (General Heterogeneous Information Network Embedding) and AHINE (Adaptive Heterogeneous Information Network Embedding), to compute distributed representations for elements in heterogeneous networks. Specially, AHINE uses an adaptive deep model to learn network embeddings that maximizes the likelihood of preserving the relationship chains between non-adjacent nodes. We apply our embeddings to a large network of points of interest (POIs) and achieve superior accuracy on some prediction problems on a ride-hailing platform. In addition, we show that AHINE outperforms state-of-the-art methods on a set of learning tasks on public datasets, including node labelling and similarity ranking in bibliographic networks.