AHINE: Adaptive Heterogeneous Information Network Embedding

TL;DR

This paper introduces AHINE, an adaptive deep learning method for embedding heterogeneous networks, which outperforms existing techniques in various network analysis tasks by effectively capturing complex relationship chains.

Contribution

The paper presents AHINE, a novel adaptive deep model for heterogeneous network embedding that enhances the preservation of relationship chains and improves task performance.

Findings

AHINE outperforms state-of-the-art methods on public datasets.

AHINE achieves higher accuracy in node labeling and similarity ranking.

Application to POI networks improves prediction accuracy.

Abstract

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.