EU institution

This is the email communication network of a large, undisclosed European institution. Nodes represent individual persons. Edges between two persons are directed and denote that at least one email has been sent from one person to the other. All edges are simple: Even if a person has sent multiple emails to another person, the two persons will be connected only by a single edge in that direction. Spam emails have been removed from the dataset.


Internal nameemail-EuAll
NameEU institution
Data source
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Communication network
Dataset timestamp 2003-01-01 ⋯ 2005-01-01
Node meaningUser
Edge meaningEmail
Network formatUnipartite, directed
Edge typeUnweighted, no multiple edges
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsContains loops
Snapshot Is a snapshot and likely to not contain all data
Multiplicity Does not have multiple edges, but the underlying data has


Size n =265,214
Volume m =420,045
Wedge count s =195,288,557
Claw count z =250,257,426,117
Cross count x =330,517,523,039,094
Triangle count t =267,313
Square count q =18,421,946
4-Tour count T4 =929,258,758
Maximum degree dmax =7,636
Maximum outdegree d+max =930
Maximum indegree dmax =7,631
Average degree d =3.167 59
Fill p =5.971 77 × 10−6
Size of LCC N =224,832
Size of LSCC Ns =34,203
Relative size of LSCC Nrs =0.128 964
Diameter δ =14
50-Percentile effective diameter δ0.5 =3.549 11
90-Percentile effective diameter δ0.9 =4.439 88
Median distance δM =4
Mean distance δm =4.080 16
Gini coefficient G =0.663 243
Relative edge distribution entropy Her =0.798 488
Power law exponent γ =6.649 66
Tail power law exponent γt =2.931 00
Tail power law exponent with p γ3 =2.931 00
p-value p =0.000 00
Outdegree tail power law exponent with p γ3,o =2.971 00
Outdegree p-value po =0.000 00
Indegree tail power law exponent with p γ3,i =2.661 00
Indegree p-value pi =0.000 00
Degree assortativity ρ =−0.178 125
Degree assortativity p-value pρ =0.000 00
In/outdegree correlation ρ± =+0.145 893
Clustering coefficient c =0.004 106 43
Spectral norm α =152.046
Operator 2-norm ν =87.357 1
Cyclic eigenvalue π =69.932 3
Algebraic connectivity a =0.000 202 791
Spectral separation 1[A] / λ2[A]| =1.602 95
Reciprocity y =0.261 970
Non-bipartivity bA =0.425 255
Normalized non-bipartivity bN =9.817 48 × 10−5
Spectral bipartite frustration bK =1.619 03 × 10−5
Controllability C =245,791


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

Hop distribution

In/outdegree scatter plot

Clustering coefficient 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] Jure Leskovec, Jon Kleinberg, and Christos Faloutsos. Graph evolution: Densification and shrinking diameters. ACM Trans. Knowl. Discov. from Data, 1(1):1–40, 2007.