This is the reply network of the social news website Digg. Each node in the network is a user of the website, and each directed edge denotes that a user replied to another user.


Internal namemunmun_digg_reply
Data sourcehttp://www.public.asu.edu/~mdechoud/datasets.html
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Communication network
Dataset timestamp 2009
Node meaningUser
Edge meaningReply
Network formatUnipartite, directed
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsContains loops
Completeness Is incomplete


Size n =30,398
Volume m =87,627
Unique edge count m̿ =86,404
Loop count l =1,424
Wedge count s =2,294,667
Claw count z =66,161,072
Cross count x =2,478,004,006
Triangle count t =4,282
Square count q =78,931
4-Tour count T4 =9,980,426
Maximum degree dmax =310
Maximum outdegree d+max =259
Maximum indegree dmax =243
Average degree d =5.765 31
Fill p =9.350 69 × 10−5
Average edge multiplicity m̃ =1.014 15
Size of LCC N =29,652
Size of LSCC Ns =6,746
Relative size of LSCC Nrs =0.221 922
Diameter δ =12
50-Percentile effective diameter δ0.5 =4.195 73
90-Percentile effective diameter δ0.9 =5.401 91
Median distance δM =5
Mean distance δm =4.680 37
Gini coefficient G =0.631 648
Relative edge distribution entropy Her =0.919 982
Power law exponent γ =1.987 46
Tail power law exponent γt =2.691 00
Degree assortativity ρ =+0.004 633 88
Degree assortativity p-value pρ =0.055 833 5
In/outdegree correlation ρ± =+0.157 008
Clustering coefficient c =0.005 598 20
Spectral norm α =40.400 0
Operator 2-norm ν =26.656 7
Cyclic eigenvalue π =17.141 8
Algebraic connectivity a =0.065 238 5
Spectral separation 1[A] / λ2[A]| =1.142 43
Reciprocity y =0.015 520 1
Non-bipartivity bA =0.338 291
Normalized non-bipartivity bN =0.037 413 4
Spectral bipartite frustration bK =0.002 814 78
Controllability C =16,796
Relative controllability Cr =0.552 536


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

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree distribution

Temporal distribution

Temporal hop distribution

Diameter/density evolution


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] Munmun De Choudhury, Hari Sundaram, Ajita John, and Dorée Duncan Seligmann. Social synchrony: Predicting mimicry of user actions in online social media. In Proc. Int. Conf. on Comput. Science and Engineering, pages 151–158, 2009.