Hypertext 2009

This is the network of face-to-face contacts of the attendees of the ACM Hypertext 2009 conference. The ACM Conference on Hypertext and Hypermedia 2009 (HT 2009, http://www.ht2009.org/) was held in Turin, Italy over three days from June 29 to July 1, 2009. In the network, a node represents a conference visitor, and an edge represents a face-to-face contact that was active for at least 20 seconds. Multiple edges denote multiple contacts. Each edge is annotated with the time at which the contact took place.


Internal namesociopatterns-hypertext
NameHypertext 2009
Data sourcehttp://www.sociopatterns.org/
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Human contact network
Dataset timestamp 2009-06-29 ⋯ 2009-07-01
Node meaningVisitor
Edge meaningContact
Network formatUnipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
LoopsDoes not contain loops


Size n =113
Volume m =20,818
Unique edge count m̿ =2,196
Loop count l =0
Wedge count s =102,182
Claw count z =1,810,498
Cross count x =26,678,148
Triangle count t =16,867
Square count q =558,736
4-Tour count T4 =4,883,008
Maximum degree dmax =1,483
Average degree d =368.460
Fill p =0.347 029
Average edge multiplicity m̃ =9.479 96
Size of LCC N =113
Diameter δ =3
50-Percentile effective diameter δ0.5 =1.160 76
90-Percentile effective diameter δ0.9 =1.841 12
Median distance δM =2
Mean distance δm =1.591 31
Gini coefficient G =0.457 576
Relative edge distribution entropy Her =0.976 022
Power law exponent γ =1.283 93
Tail power law exponent γt =4.171 00
Degree assortativity ρ =−0.122 580
Degree assortativity p-value pρ =3.576 18 × 10−16
Clustering coefficient c =0.495 205
Spectral norm α =1,291.72
Algebraic connectivity a =2.013 10
Spectral separation 1[A] / λ2[A]| =1.006 03
Non-bipartivity bA =0.005 997 03
Normalized non-bipartivity bN =0.764 945
Algebraic non-bipartivity χ =0.989 464
Spectral bipartite frustration bK =0.006 364 38
Controllability C =0
Relative controllability Cr =0.000 00


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

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] Lorenzo Isella, Juliette Stehlé, Alain Barrat, Ciro Cattuto, Jean-François Pinton, and Wouter Van den Broeck. What's in a crowd? analysis of face-to-face behavioral networks. J. of Theoretical Biology, 271(1):166–180, 2011.