GAHNE: Graph-Aggregated Heterogeneous Network Embedding
This work provides an incremental improvement in heterogeneous network embedding for researchers and practitioners working with complex, multi-typed network data.
This paper addresses the challenge of heterogeneous network embedding by proposing GAHNE, a model that comprehensively extracts semantics from heterogeneous information networks. It achieves state-of-art performance on three real-world HIN datasets.
The real-world networks often compose of different types of nodes and edges with rich semantics, widely known as heterogeneous information network (HIN). Heterogeneous network embedding aims to embed nodes into low-dimensional vectors which capture rich intrinsic information of heterogeneous networks. However, existing models either depend on manually designing meta-paths, ignore mutual effects between different semantics, or omit some aspects of information from global networks. To address these limitations, we propose a novel Graph-Aggregated Heterogeneous Network Embedding (GAHNE), which is designed to extract the semantics of HINs as comprehensively as possible to improve the results of downstream tasks based on graph convolutional neural networks. In GAHNE model, we develop several mechanisms that can aggregate semantic representations from different single-type sub-networks as well as fuse the global information into final embeddings. Extensive experiments on three real-world HIN datasets show that our proposed model consistently outperforms the existing state-of-the-art methods.