A Tutorial on Network Embeddings
Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visualization. In this survey, we give an overview of netw...
Saved in:
Main Authors | , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
07.08.2018
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Network embedding methods aim at learning low-dimensional latent
representation of nodes in a network. These representations can be used as
features for a wide range of tasks on graphs such as classification,
clustering, link prediction, and visualization. In this survey, we give an
overview of network embeddings by summarizing and categorizing recent
advancements in this research field. We first discuss the desirable properties
of network embeddings and briefly introduce the history of network embedding
algorithms. Then, we discuss network embedding methods under different
scenarios, such as supervised versus unsupervised learning, learning embeddings
for homogeneous networks versus for heterogeneous networks, etc. We further
demonstrate the applications of network embeddings, and conclude the survey
with future work in this area. |
---|---|
AbstractList | Network embedding methods aim at learning low-dimensional latent
representation of nodes in a network. These representations can be used as
features for a wide range of tasks on graphs such as classification,
clustering, link prediction, and visualization. In this survey, we give an
overview of network embeddings by summarizing and categorizing recent
advancements in this research field. We first discuss the desirable properties
of network embeddings and briefly introduce the history of network embedding
algorithms. Then, we discuss network embedding methods under different
scenarios, such as supervised versus unsupervised learning, learning embeddings
for homogeneous networks versus for heterogeneous networks, etc. We further
demonstrate the applications of network embeddings, and conclude the survey
with future work in this area. |
Author | Perozzi, Bryan Skiena, Steven Chen, Haochen Al-Rfou, Rami |
Author_xml | – sequence: 1 givenname: Haochen surname: Chen fullname: Chen, Haochen – sequence: 2 givenname: Bryan surname: Perozzi fullname: Perozzi, Bryan – sequence: 3 givenname: Rami surname: Al-Rfou fullname: Al-Rfou, Rami – sequence: 4 givenname: Steven surname: Skiena fullname: Skiena, Steven |
BackLink | https://doi.org/10.48550/arXiv.1808.02590$$DView paper in arXiv |
BookMark | eNotzjkOwjAQQFEXULAdgApfIMF2GNspEWKTImjSR-OMQRGQILPfHrFUv_t6Xdaqm9ozNpQinlgAMcbwrO6xtMLGQkEqOoxPeX67NqHCI29qvvHXRxMOfH5ynqiq95c-a-_wePGDf3ssX8zz2SrKtsv1bJpFKCWISJeAqTISnRJGAmg0JiFfGjEBInJ6lyYlOakAELVVxsrUWgJyFjzppMdGv-2XWJxDdcLwKj7U4ktN3qroOQg |
CitedBy_id | crossref_primary_10_3389_fncom_2022_1024205 |
ContentType | Journal Article |
Copyright | http://arxiv.org/licenses/nonexclusive-distrib/1.0 |
Copyright_xml | – notice: http://arxiv.org/licenses/nonexclusive-distrib/1.0 |
DBID | AKY GOX |
DOI | 10.48550/arxiv.1808.02590 |
DatabaseName | arXiv Computer Science arXiv.org |
DatabaseTitleList | |
Database_xml | – sequence: 1 dbid: GOX name: arXiv.org url: http://arxiv.org/find sourceTypes: Open Access Repository |
DeliveryMethod | fulltext_linktorsrc |
ExternalDocumentID | 1808_02590 |
GroupedDBID | AKY GOX |
ID | FETCH-LOGICAL-a1150-6c5a9271ab2071556a773dec7045dddb6f93cdb1255aa682781988d5db85ed63 |
IEDL.DBID | GOX |
IngestDate | Mon Jan 08 05:49:02 EST 2024 |
IsDoiOpenAccess | true |
IsOpenAccess | true |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-a1150-6c5a9271ab2071556a773dec7045dddb6f93cdb1255aa682781988d5db85ed63 |
OpenAccessLink | https://arxiv.org/abs/1808.02590 |
ParticipantIDs | arxiv_primary_1808_02590 |
PublicationCentury | 2000 |
PublicationDate | 2018-08-07 |
PublicationDateYYYYMMDD | 2018-08-07 |
PublicationDate_xml | – month: 08 year: 2018 text: 2018-08-07 day: 07 |
PublicationDecade | 2010 |
PublicationYear | 2018 |
Score | 1.7113235 |
SecondaryResourceType | preprint |
Snippet | Network embedding methods aim at learning low-dimensional latent
representation of nodes in a network. These representations can be used as
features for a wide... |
SourceID | arxiv |
SourceType | Open Access Repository |
SubjectTerms | Computer Science - Social and Information Networks |
Title | A Tutorial on Network Embeddings |
URI | https://arxiv.org/abs/1808.02590 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwdV09T8MwED2VTiwIBKh8ygOrRRPHHxkr1FIhUZYgZYvs3EVioKBSED-_ZycIFlbby_mG90737h3AjWoZFckpGahAWfiAMmiNkjrVqVZjUaYhsceVWT4XD7WuRyB-ZmH85vvlq_cHDh-3mYtSR2boXJTv5XmUbN0_1X1zMllxDe9_3zHHTEd_QGJxCAcDuxOzPh1HMKL1MYiZqKJTAGdavK3Fqhdei_lrIEydnxOoFvPqbimHxQTSRwIlTat9mdvMh5wRWmvjrVVIrWV-hBgH20rVYmDuoL03LrcMu86hxuA0oVGnMObaniYgCj3FachcGbdxU5c7so4yTyp0nTEaz2CSwmnee--JJkbapEjP_7-6gH3GdZd0avYSxtvNJ10xdm7DdfrAHZvfbYk |
link.rule.ids | 228,230,786,891 |
linkProvider | Cornell University |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+Tutorial+on+Network+Embeddings&rft.au=Chen%2C+Haochen&rft.au=Perozzi%2C+Bryan&rft.au=Al-Rfou%2C+Rami&rft.au=Skiena%2C+Steven&rft.date=2018-08-07&rft_id=info:doi/10.48550%2Farxiv.1808.02590&rft.externalDocID=1808_02590 |