Digg votes

These are votes on stories by users of Digg. The network is bipartite, and nodes represent users and stories, and each edge represents one vote. The data covers the period of a month in 2009. The dataset contains multiple edges, when a single user has apparently given multiple votes to a single item.


Internal namedigg-votes
NameDigg votes
Data sourcehttp://www.isi.edu/~lerman/downloads/digg2009.html
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Rating network
Dataset timestamp 2009
Node meaningUser, story
Edge meaningVote
Network formatBipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
Snapshot Is a snapshot and likely to not contain all data


Size n =142,962
Left size n1 =139,409
Right size n2 =3,553
Volume m =3,018,197
Unique edge count m̿ =3,010,898
Wedge count s =3,452,748,079
Claw count z =5,552,161,405,338
Cross count x =17,710,305,006,153,294
Square count q =29,370,076,933
4-Tour count T4 =248,779,120,872
Maximum degree dmax =24,099
Maximum left degree d1max =10,526
Maximum right degree d2max =24,099
Average degree d =42.223 8
Average left degree d1 =21.649 9
Average right degree d2 =849.478
Fill p =0.006 078 69
Average edge multiplicity m̃ =1.002 42
Size of LCC N =142,962
Diameter δ =4
50-Percentile effective diameter δ0.5 =3.398 00
90-Percentile effective diameter δ0.9 =3.879 60
Median distance δM =4
Mean distance δm =3.707 10
Gini coefficient G =0.882 325
Balanced inequality ratio P =0.126 496
Left balanced inequality ratio P1 =0.177 512
Right balanced inequality ratio P2 =0.333 871
Relative edge distribution entropy Her =0.820 754
Power law exponent γ =1.572 35
Tail power law exponent γt =1.771 00
Degree assortativity ρ =−0.179 694
Degree assortativity p-value pρ =0.000 00
Spectral norm α =604.628
Algebraic connectivity a =0.631 175


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

Temporal distribution

Temporal hop distribution

Diameter/density evolution

Inter-event distribution

Node-level inter-event 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] T. Hogg and K. Lerman. Social dynamics of Digg. Eur. Phys. J. Data Sci., 1(5), 2012.