David Copperfield

This is the undirected network of common noun and adjective adjacencies for the novel David Copperfield by English 19th century writer Charles Dickens. A node represents either a noun or an adjective. An edge connects two words that occur in adjacent positions. The network is not bipartite, i.e., there are edges connecting adjectives with adjectives, nouns with nouns and adjectives with nouns.


Internal nameadjnoun_adjacency
NameDavid Copperfield
Data sourcehttp://www-personal.umich.edu/~mejn/netdata/adjnoun.zip
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Lexical network
Node meaningWord
Edge meaningAdjacency
Network formatUnipartite, undirected
Edge typeUnweighted, no multiple edges
LoopsDoes not contain loops
Orientation Is not directed, but the underlying data is


Size n =112
Volume m =425
Loop count l =0
Wedge count s =5,429
Claw count z =39,977
Cross count x =318,768
Triangle count t =284
Square count q =2,579
4-Tour count T4 =43,198
Maximum degree dmax =49
Average degree d =7.589 29
Fill p =0.068 371 9
Size of LCC N =112
Diameter δ =5
50-Percentile effective diameter δ0.5 =1.931 24
90-Percentile effective diameter δ0.9 =2.980 60
Median distance δM =2
Mean distance δm =2.472 39
Gini coefficient G =0.417 311
Balanced inequality ratio P =0.345 882
Relative edge distribution entropy Her =0.934 657
Power law exponent γ =1.589 57
Tail power law exponent γt =3.621 00
Degree assortativity ρ =−0.129 348
Degree assortativity p-value pρ =0.000 155 916
Clustering coefficient c =0.156 935
Spectral norm α =13.150 2
Algebraic connectivity a =0.695 020
Non-bipartivity bA =0.420 969
Normalized non-bipartivity bN =0.276 497
Algebraic non-bipartivity χ =0.675 374
Spectral bipartite frustration bK =0.022 247 6
Controllability C =6
Relative controllability Cr =0.053 571 4


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] Mark E. J. Newman. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E, 74(3), 2006.