This undirected network represents contacts between people measured by carried wireless devices. A node represents a person; an edge between two persons shows that there was a contact between them.


Internal namecontact
Data sourcehttp://www.cl.cam.ac.uk/research/srg/netos/haggle/
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Human contact network
Node meaningPerson
Edge meaningContact
Network formatUnipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
LoopsDoes not contain loops


Size n =274
Volume m =28,244
Unique edge count m̿ =2,899
Loop count l =0
Wedge count s =118,281
Claw count z =9,250,623
Cross count x =255,553,498
Triangle count t =22,332
Square count q =848,067
4-Tour count T4 =7,261,908
Maximum degree dmax =2,092
Average degree d =206.161
Fill p =0.077 511 3
Average edge multiplicity m̃ =9.742 67
Size of LCC N =274
Diameter δ =4
50-Percentile effective diameter δ0.5 =1.949 57
90-Percentile effective diameter δ0.9 =2.792 77
Median distance δM =2
Mean distance δm =2.415 27
Gini coefficient G =0.841 739
Balanced inequality ratio P =0.110 360
Relative edge distribution entropy Her =0.794 985
Power law exponent γ =1.672 86
Tail power law exponent γt =1.501 00
Tail power law exponent with p γ3 =1.501 00
p-value p =0.000 00
Degree assortativity ρ =−0.474 322
Degree assortativity p-value pρ =2.635 98 × 10−237
Clustering coefficient c =0.566 414
Spectral norm α =1,231.03
Algebraic connectivity a =0.992 336
Spectral separation 1[A] / λ2[A]| =5.811 49
Non-bipartivity bA =0.827 927
Normalized non-bipartivity bN =0.466 542
Algebraic non-bipartivity χ =0.827 831
Spectral bipartite frustration bK =0.013 349 0
Controllability C =192
Relative controllability Cr =0.700 730


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

Edge weight/multiplicity distribution

Clustering coefficient distribution

Average neighbor degree 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] Augustin Chaintreau, Pan Hui, Jon Crowcroft, Christophe Diot, Richard Gass, and James Scott. Impact of human mobility on opportunistic forwarding algorithms. IEEE Trans. on Mobile Comput., 6(6):606–620, 2007.