NDLI: Embedding of Embedding (EOE): Joint Embedding for Coupled Heterogeneous Networks

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Embedding of Embedding (EOE): Joint Embedding for Coupled Heterogeneous Networks

Content Provider	ACM Digital Library
Author	Cao, Jiannong Yu, Philip S. Wei, Xiaokai Xu, Linchuan
Abstract	Network embedding is increasingly employed to assist network analysis as it is effective to learn latent features that encode linkage information. Various network embedding methods have been proposed, but they are only designed for a single network scenario. In the era of big data, different types of related information can be fused together to form a coupled 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 prediction multi-class classification, and multi-label classification.
Starting Page	741
Ending Page	749
Page Count	9
File Format	PDF
ISBN	9781450346757
DOI	10.1145/3018661.3018723
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2017-02-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Network embedding Data mining Coupled heterogeneous networks
Content Type	Text
Resource Type	Article

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