articleNov 6, 2017Closed access
HIN2Vec
Indexed incrossref
Abstract
In this paper, we propose a novel representation learning framework, namely HIN2Vec, for heterogeneous information networks (HINs). The core of the proposed framework is a neural network model, also called HIN2Vec, designed to capture the rich semantics embedded in HINs by exploiting different types of relationships among nodes. Given a set of relationships specified in forms of meta-paths in an HIN, HIN2Vec carries out multiple prediction training tasks jointly based on a target set of relationships to learn latent vectors of nodes and meta-paths in the HIN. In addition to model design, several issues unique to HIN2Vec, including regularization of meta-path vectors, node type selection in negative sampling,…
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618
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Authors
3Topics & keywords
Topics
Keywords
- Computer science
- Node (physics)
- Semantics (computer science)
- Artificial intelligence
- Feature learning
- Representation (politics)
- Regularization (linguistics)
- Set (abstract data type)
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