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Erschienen in:
A General Fine-Grained Truth Discovery Approach for Crowdsourced Data Aggregation Kapitel lesen Erstes Kapitel lesen

2017 | OriginalPaper | Buchkapitel

HINE: Heterogeneous Information Network Embedding

verfasst von : Yuxin Chen, Chenguang Wang

Erschienen in: Database Systems for Advanced Applications

Verlag: Springer International Publishing

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Abstract

Network embedding has shown its effectiveness in embedding homogeneous networks. Compared with homogeneous networks, heterogeneous information networks (HINs) contain semantic information from multi-typed entities and relations, and are shown to be a more effective model for real world data. The existing network embedding methods fail to explicitly capture the semantics in HINs. In this paper, we propose an HIN embedding model (HINE), which consists of local and global semantic embedding. Local semantic embedding aims to incorporate entity type information via embedding the local structures and types of the entities in a supervised way. Global semantic embedding leverages multi-hop relation types among entities to propagate the global semantics via a Markov Random Field (MRF) to impact the embedding vectors. By doing so, HINE is capable to capture both local and global semantic information in the embedding vectors. Experimental results show that HINE significantly outperforms state-of-the-art methods.

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Vorheriges Kapitel PPNE: Property Preserving Network Embedding
Nächstes Kapitel DT-KST: Distributed Top-k Similarity Query on Big Trajectory Streams
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Metadaten
Titel
HINE: Heterogeneous Information Network Embedding
verfasst von
Yuxin Chen
Chenguang Wang
Copyright-Jahr
2017
Buch
Database Systems for Advanced Applications

Print ISBN: 978-3-319-55752-6

Electronic ISBN: 978-3-319-55753-3

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-55753-3_12

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