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Relation Structure-Aware Heterogeneous Information Network Embedding | Yuanfu Lu
; Chuan Shi
; Linmei Hu
; Zhiyuan Liu
; | Date: |
15 May 2019 | Abstract: | Heterogeneous information network (HIN) embedding aims to embed multiple
types of nodes into a low-dimensional space. Although most existing HIN
embedding methods consider heterogeneous relations in HINs, they usually employ
one single model for all relations without distinction, which inevitably
restricts the capability of network embedding. In this paper, we take the
structural characteristics of heterogeneous relations into consideration and
propose a novel Relation structure-aware Heterogeneous Information Network
Embedding model (RHINE). By exploring the real-world networks with thorough
mathematical analysis, we present two structure-related measures which can
consistently distinguish heterogeneous relations into two categories:
Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the
distinctive characteristics of relations, in our RHINE, we propose different
models specifically tailored to handle ARs and IRs, which can better capture
the structures and semantics of the networks. At last, we combine and optimize
these models in a unified and elegant manner. Extensive experiments on three
real-world datasets demonstrate that our model significantly outperforms the
state-of-the-art methods in various tasks, including node clustering, link
prediction, and node classification. | Source: | arXiv, 1905.8027 | Services: | Forum | Review | PDF | Favorites |
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