Daily Kos

This is the bipartite document–word dataset of Daily Kos. Left nodes are documents and right nodes are words. Edge weights are multiplicities.


Internal namebag-kos
NameDaily Kos
Data sourcehttp://archive.ics.uci.edu/ml/datasets/Bag+of+Words
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Text network
Node meaningDocument, word
Edge meaningOccurrence
Network formatBipartite, undirected
Edge typeUnweighted, multiple edges


Size n =10,336
Left size n1 =3,430
Right size n2 =6,906
Volume m =467,714
Unique edge count m̿ =353,160
Wedge count s =68,241,250
Claw count z =11,455,280,341
Cross count x =2,925,573,347,776
Square count q =467,702,668
4-Tour count T4 =4,015,445,872
Maximum degree dmax =2,123
Maximum left degree d1max =457
Maximum right degree d2max =2,123
Average degree d =90.501 9
Average left degree d1 =136.360
Average right degree d2 =67.725 7
Fill p =0.014 909 1
Average edge multiplicity m̃ =1.324 37
Size of LCC N =10,336
Diameter δ =5
50-Percentile effective diameter δ0.5 =2.596 43
90-Percentile effective diameter δ0.9 =3.691 70
Median distance δM =3
Mean distance δm =3.078 07
Gini coefficient G =0.606 086
Balanced inequality ratio P =0.269 422
Left balanced inequality ratio P1 =0.364 120
Right balanced inequality ratio P2 =0.246 890
Relative edge distribution entropy Her =0.940 178
Power law exponent γ =1.274 22
Tail power law exponent γt =2.351 00
Degree assortativity ρ =−0.054 950 6
Degree assortativity p-value pρ =0.000 00
Spectral norm α =358.079
Algebraic connectivity a =5.811 82


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

Delaunay graph drawing

Edge weight/multiplicity 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] M. Lichman. UCI Machine Learning Repository, 2013. [ http ]