research-article

Network Embedding on Hierarchical Community Structure Network

Authors:

Junshan WangAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 15, Issue 4

Article No.: 62, Pages 1 - 23

https://doi.org/10.1145/3434747

Published: 08 May 2021 Publication History

Abstract

Network embedding is a method of learning a low-dimensional vector representation of network vertices under the condition of preserving different types of network properties. Previous studies mainly focus on preserving structural information of vertices at a particular scale, like neighbor information or community information, but cannot preserve the hierarchical community structure, which would enable the network to be easily analyzed at various scales. Inspired by the hierarchical structure of galaxies, we propose the Galaxy Network Embedding (GNE) model, which formulates an optimization problem with spherical constraints to describe the hierarchical community structure preserving network embedding. More specifically, we present an approach of embedding communities into a low-dimensional spherical surface, the center of which represents the parent community they belong to. Our experiments reveal that the representations from GNE preserve the hierarchical community structure and show advantages in several applications such as vertex multi-class classification, network visualization, and link prediction. The source code of GNE is available online.

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Cited By

Zhao NLiu QJing MLi JZhao ZWang J(2022)DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index FusionApplied Sciences10.3390/app1201052212:1(522)Online publication date: 5-Jan-2022
https://doi.org/10.3390/app12010522
Liu YLiu JWang ZWang XZhang FXiao Z(2022)Binarized network embedding with community structural informationInformation Sciences: an International Journal10.1016/j.ins.2022.09.055616:C(204-216)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.09.055

Index Terms

Network Embedding on Hierarchical Community Structure Network
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 15, Issue 4

August 2021

486 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3458847

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

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Copyright © 2021 ACM.

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Publication History

Published: 08 May 2021

Accepted: 01 November 2020

Revised: 01 September 2020

Received: 01 September 2019

Published in TKDD Volume 15, Issue 4

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Cited By

Zhao NLiu QJing MLi JZhao ZWang J(2022)DDMF: A Method for Mining Relatively Important Nodes Based on Distance Distribution and Multi-Index FusionApplied Sciences10.3390/app1201052212:1(522)Online publication date: 5-Jan-2022
https://doi.org/10.3390/app12010522
Liu YLiu JWang ZWang XZhang FXiao Z(2022)Binarized network embedding with community structural informationInformation Sciences: an International Journal10.1016/j.ins.2022.09.055616:C(204-216)Online publication date: 1-Nov-2022
https://dl.acm.org/doi/10.1016/j.ins.2022.09.055

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