NIPS full papers

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

Metadata

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

Statistics

Size n =13,875
Left size n1 =1,500
Right size n2 =12,375
Volume m =1,932,365
Unique edge count m̿ =746,316
Wedge count s =316,414,350
Claw count z =60,669,295,416
Cross count x =10,271,177,488,043
Square count q =7,325,274,840
Maximum degree dmax =1,455
Maximum left degree d1max =914
Maximum right degree d2max =1,455
Average degree d =278.539
Average left degree d1 =1,288.24
Average right degree d2 =156.151
Fill p =0.040 205 6
Average edge multiplicity m̃ =2.589 20
Size of LCC N =13,875
Diameter δ =6
50-Percentile effective diameter δ0.5 =3.028 37
90-Percentile effective diameter δ0.9 =3.805 67
Median distance δM =4
Mean distance δm =3.219 19
Gini coefficient G =0.821 596
Balanced inequality ratio P =0.140 265
Left balanced inequality ratio P1 =0.454 947
Right balanced inequality ratio P2 =0.178 579
Relative edge distribution entropy Her =0.892 567
Power law exponent γ =1.300 92
Degree assortativity ρ =−0.042 518 0
Degree assortativity p-value pρ =0.000 00
Spectral norm α =1,943.24

Plots

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

Downloads

References

[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 ]