Enron words

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


Internal namebag-enron
NameEnron words
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 =67,960
Left size n1 =39,861
Right size n2 =28,099
Volume m =6,412,172
Unique edge count m̿ =3,710,420
Wedge count s =3,214,624,476
Claw count z =2,510,007,422,598
Cross count x =2,191,825,474,071,012
4-Tour count T4 =376,634,510,028
Maximum degree dmax =7,190
Maximum left degree d1max =2,120
Maximum right degree d2max =7,190
Average degree d =188.704
Average left degree d1 =160.863
Average right degree d2 =228.199
Fill p =0.003 312 71
Average edge multiplicity m̃ =1.728 15
Size of LCC N =67,960
Diameter δ =6
50-Percentile effective diameter δ0.5 =2.492 21
90-Percentile effective diameter δ0.9 =3.606 21
Median distance δM =3
Mean distance δm =2.992 72
Gini coefficient G =0.707 894
Balanced inequality ratio P =0.224 254
Left balanced inequality ratio P1 =0.225 645
Right balanced inequality ratio P2 =0.156 346
Relative edge distribution entropy Her =0.897 344
Power law exponent γ =1.269 14
Tail power law exponent γt =1.991 00
Degree assortativity ρ =−0.174 109
Degree assortativity p-value pρ =0.000 00
Spectral separation 1[A] / λ2[A]| =1.700 69
Controllability C =14,724
Relative controllability Cr =0.216 657


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

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 ]