Embedding of embedding (EOE) : Joint embedding for coupled heterogeneous networks

Network embedding is increasingly employed to assist net- work analysis as it is effective to learn latent features that en- code linkage information. Various network embedding meth- ods have been proposed, but they are only designed for a sin- gle network scenario. In the era of big data, different types of related information can be fused together to form a cou- pled heterogeneous network, which consists of two different but related sub-networks connected by inter-network edges. In this scenario, the inter-network edges can act as comple- mentary information in the presence of intra-network ones. This complementary information is important because it can make latent features more comprehensive and accurate. And it is more important when the intra-network edges are ab- sent, which can be referred to as the cold-start problem. In this paper, we thus propose a method named embedding of embedding (EOE) for coupled heterogeneous networks. In the EOE, latent features encode not only intra-network edges, but also inter-network ones. To tackle the challenge of heterogeneities of two networks, the EOE incorporates a harmonious embedding matrix to further embed the em- beddings that only encode intra-network edges. Empirical experiments on a variety of real-world datasets demonstrate the EOE outperforms consistently single network embedding methods in applications including visualization, link predic- tion multi-class classification, and multi-label classification.