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ArXivExplore

Guides

  • ArXivArticles
  • ArXivAuthor
  • ArXivCitations
  • ArXivClassify
  • ArXivCluster
  • ArXivDataset
  • ArXivExplain
  • ArXivFeatures
  • ArXivLogos
  • ArXivTeX
  • ArXivTop
  • ArXivTrend

Symbols

  • ArXivAbstracts
  • ArXivArticles
  • ArXivAuthorArticles
  • ArXivAuthorGraph
  • ArXivAuthorRegister
  • ArXivAuthors
  • ArXivCategoriesLegend
  • ArXivCategories
  • ArXivCategoriesPrimary
  • ArXivCitationsAbstracts
  • ArXivCitationsAuthor
  • ArXivCitationsGraph
  • ArXivCitations
  • ArXivCitationsTitles
  • ArXivCitationsTotal
  • ArXivClassifyAuthorsNet
  • ArXivClassifyAuthorsTrainTest
  • ArXivClassifyCategoriesNet
  • ArXivClassifyCategoriesTrainTest
  • ArXivClusterAuthorsGraph
  • ArXivClusterCategoriesGraph
  • ArXivClusterLegend
  • ArXivDatasetAggregate
  • ArXivDatasetLookup
  • ArXivDataset
  • ArXivDatasetSubset
  • ArXivDates
  • ArXivExplainAuthor
  • ArXivExplainConcept
  • ArXivFeaturesAuthorsNearest
  • ArXivFeaturesAuthorsNet
  • ArXivFeaturesAuthorsSet
  • ArXivFeaturesCategoriesNearest
  • ArXivFeaturesCategoriesNet
  • ArXivFeaturesCategoriesSet
  • ArXivIDs
  • ArXivLogosAbstracts
  • ArXivLogosTitles
  • ArXivPlotAbstracts
  • ArXivPlot
  • ArXivPlotTitles
  • ArXivTeXDocument
  • ArXivTeXFormulae
  • ArXivTeXIntroduction
  • ArXivTeXPreamble
  • ArXivTeXSections
  • ArXivTitles
  • ArXivTopAbstracts
  • ArXivTopAuthors
  • ArXivTopCategories
  • ArXivTopTitles
  • ArXivTrendAbstracts
  • ArXivTrendArticles
  • ArXivTrendTitles
  • ArXivVersions
  • ArXivVocabularyAbstracts
  • ArXivVocabularyIntroductions
  • ArXivVocabularyTitles
DanieleGregori`ArXivExplore`
ArXivClusterAuthorsGraph
​
ArXivClusterAuthorsGraph
[set_Association,k_Integer]
clusters the articles in association 'set' by using a feature extractor net trained on authors classification. The clustering is done by choosing the 'k' nearest features to each article;
​
​
ArXivClusterAuthorsGraph
[net_NetGraph,set_Association,k_Integer]
clusters the articles in association 'set' by using a feature extractor net 'net' trained on authors classification. The clustering is done by choosing the 'k' nearest features to each article.
​
Details and Options

Examples  
(1)
Basic Examples  
(1)
In[1]:=
setAut=
ArXivFeaturesAuthorsSet

Sharif (gr)
ARXIVAUTHOR
,
Cardoso (gr)
ARXIVAUTHOR
,
Lu (het)
ARXIVAUTHOR
,
Vafa (het)
ARXIVAUTHOR
,10,{Yellow,Red,Purple,Blue};
In[2]:=
featAuthors=
ArXivFeaturesAuthorsNet
​​ 
Sharif (gr)
ARXIVAUTHOR
,
Cardoso (gr)
ARXIVAUTHOR
,
Lu (het)
ARXIVAUTHOR
,
Vafa (het)
ARXIVAUTHOR

Out[2]=
In[3]:=
vf2[{xc_,yc_},name_,{w_,h_}]:=TooltipDisk[{xc,yc},Sqrt[w^2+h^2]],TableForm@
ArXivArticles
@name1
In[4]:=
ArXivClusterAuthorsGraph
[featAuthors,setAut,15,VertexShapeFunctionvf2,VertexLabelsAutomatic,EdgeStyleThin]
Out[4]=
""

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