Representation learning for large-scale dynamic networks

Yanwei Yu, Huaxiu Yao, Hongjian Wang, Xianfeng Tang, Zhenhui Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Representation leaning on networks aims to embed networks into a low-dimensional vector space, which is useful in many tasks such as node classification, network clustering, link prediction and recommendation. In reality, most real-life networks constantly evolve over time with various kinds of changes to the network structure, e.g., creation and deletion of edges. However, existing network embedding methods learn the representation vectors for nodes in a static manner, which are not suitable for dynamic network embedding. In this paper, we propose a dynamic network embedding approach for large-scale networks. The method incrementally updates the embeddings by considering the changes of the network structures and is able to dynamically learn the embedding for networks with millions of nodes within a few seconds. Extensive experimental results on three real large-scale networks demonstrate the efficiency and effectiveness of our proposed methods.

Original languageEnglish (US)
Title of host publicationDatabase Systems for Advanced Applications - 23rd International Conference, DASFAA 2018, Proceedings
EditorsJian Pei, Shazia Sadiq, Jianxin Li, Yannis Manolopoulos
PublisherSpringer Verlag
Pages526-541
Number of pages16
ISBN (Print)9783319914572
DOIs
StatePublished - 2018
Event23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018 - Gold Coast, Australia
Duration: May 21 2018May 24 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10828 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other23rd International Conference on Database Systems for Advanced Applications, DASFAA 2018
CountryAustralia
CityGold Coast
Period5/21/185/24/18

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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