This is the bipartite network of article–word inclusions in documents that appeared on Reuters newswire in 1987. Left nodes represent articles and right nodes represent words. An edge represents an article–word inclusion.


Internal namegottron-reuters
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Text network
Node meaningArticle, word
Edge meaningInclusion
Network formatBipartite, undirected
Edge typeUnweighted, multiple edges


Size n =60,234
Left size n1 =21,557
Right size n2 =38,677
Volume m =1,464,182
Unique edge count m̿ =978,446
Wedge count s =821,566,836
Claw count z =1,978,784,882,823
Cross count x =6,916,709,380,031,432
Square count q =2,502,669,891
4-Tour count T4 =23,309,665,440
Maximum degree dmax =19,044
Maximum left degree d1max =380
Maximum right degree d2max =19,044
Average degree d =48.616 5
Average left degree d1 =67.921 4
Average right degree d2 =37.856 7
Fill p =0.001 173 53
Average edge multiplicity m̃ =1.496 44
Size of LCC N =58,433
Diameter δ =7
50-Percentile effective diameter δ0.5 =3.005 57
90-Percentile effective diameter δ0.9 =3.853 43
Median distance δM =4
Mean distance δm =3.445 74
Gini coefficient G =0.754 152
Balanced inequality ratio P =0.213 382
Left balanced inequality ratio P1 =0.333 062
Right balanced inequality ratio P2 =0.094 919 9
Relative edge distribution entropy Her =0.856 671
Power law exponent γ =1.532 15
Tail power law exponent γt =2.401 00
Degree assortativity ρ =−0.147 873
Degree assortativity p-value pρ =0.000 00
Spectral norm α =685.707
Algebraic connectivity a =0.218 874
Spectral separation 1[A] / λ2[A]| =1.592 83
Controllability C =23,631


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

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] David D. Lewis, Yiming Yang, Tony G. Rose, and Fan Li. RCV1: A new benchmark collection for text categorization research. J. Mach. Learn. Res., 5:361–397, 2004.