This is the undirected network of Flickr images sharing common metadata such as tags, groups, locations etc. A node represents an image, and an edge indicates that two images share the same metadata.


Internal nameflickrEdges
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Miscellaneous network
Node meaningImage
Edge meaningSimilarity
Network formatUnipartite, undirected
Edge typeUnweighted, no multiple edges
LoopsDoes not contain loops


Size n =105,938
Volume m =2,316,948
Loop count l =0
Wedge count s =806,678,787
Claw count z =181,947,878,743
Cross count x =65,490,641,956,676
Triangle count t =107,987,357
Square count q =34,729,244,373
4-Tour count T4 =281,065,304,028
Maximum degree dmax =5,425
Average degree d =43.741 6
Fill p =0.000 412 902
Size of LCC N =105,722
Diameter δ =9
50-Percentile effective diameter δ0.5 =3.858 26
90-Percentile effective diameter δ0.9 =4.807 41
Median distance δM =4
Mean distance δm =4.337 71
Gini coefficient G =0.780 096
Balanced inequality ratio P =0.153 442
Relative edge distribution entropy Her =0.876 354
Power law exponent γ =1.407 23
Tail power law exponent γt =1.731 00
Degree assortativity ρ =+0.246 851
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.401 600
Spectral norm α =615.564
Algebraic connectivity a =0.032 533 4
Spectral separation 1[A] / λ2[A]| =1.197 14
Non-bipartivity bA =0.829 008
Normalized non-bipartivity bN =0.066 857 3
Algebraic non-bipartivity χ =0.265 997
Spectral bipartite frustration bK =0.001 517 36
Controllability C =15,442
Relative controllability Cr =0.145 765


Fruchterman–Reingold graph drawing

Degree distribution

Cumulative degree distribution

Lorenz curve

Spectral distribution of the adjacency matrix

Spectral distribution of the normalized adjacency matrix

Spectral distribution of the Laplacian

Spectral graph drawing based on the adjacency matrix

Spectral graph drawing based on the Laplacian

Spectral graph drawing based on the normalized adjacency matrix

Degree assortativity

Zipf plot

Hop distribution

Double Laplacian graph drawing

Delaunay graph drawing

Clustering coefficient distribution

Average neighbor degree distribution


Matrix decompositions plots



[1] Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ]
[2] Julian McAuley and Jure Leskovec. Learning to discover social circles in ego networks. In Adv. in Neural Inf. Process. Syst., pages 548–556. 2012.