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An Adaptive Embedding Framework for Heterogeneous Information Networks

Published: 19 October 2020 Publication History

Abstract

Heterogeneous information networks (HINs) have been ubiquitous in the real-world. HIN embeddings, which encode various information of the networks into low-dimensional vectors, can facilitate a wide range of applications on graph-structured data. Existing HIN embedding methods include random walk based methods that may not fully utilize the edge semantics and knowledge graph embedding methods that restrict the expression ability of topological information. In this paper, we propose a novel adaptive embedding framework, which integrates these two kinds of methods to preserve both topological information and relational information. By incorporating an assistant knowledge graph embedding model, the proposed framework performs efficient biased random walk under the guidance of edge semantics.

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MP4 File (3340531.3411989.mp4)
Presentation Video for the work "An Adaptive Embedding Framework for Heterogeneous Information Networks". In this video, the authors propose a path-based relation-aware embedding framework for Heterogeneous Information Networks (HINs). They introduce how to adaptively encode both topological and relational information into HIN embeddings.

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  • (2023)UniSKGRep: A unified representation learning framework of social network and knowledge graphNeural Networks10.1016/j.neunet.2022.11.010158(142-153)Online publication date: Jan-2023

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cover image ACM Conferences
CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management
October 2020
3619 pages
ISBN:9781450368599
DOI:10.1145/3340531
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Published: 19 October 2020

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Author Tags

  1. heterogeneous network embedding
  2. knowledge graph embedding
  3. network representation learning

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  • Shenzhen General Research Project

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  • (2023)UniSKGRep: A unified representation learning framework of social network and knowledge graphNeural Networks10.1016/j.neunet.2022.11.010158(142-153)Online publication date: Jan-2023

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