WebUni Magdeburg

This is the bipartite network of threads and the words they contain from the student's social website WebUni of the Otto-von-Guericke University Magdeburg in Germany.

Metadata

CodeWU
Internal namewebuni
NameWebUni Magdeburg
Data sourcehttp://magdeburg.webuni.de/
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Text network
Node meaningThread, word
Edge meaningUse
Network formatBipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps

Statistics

Size n =206,350
Left size n1 =6,202
Right size n2 =200,148
Volume m =3,869,707
Unique edge count m̿ =1,948,004
Wedge count s =1,353,078,618
Claw count z =779,473,328,511
Cross count x =513,883,255,015,350
Square count q =21,431,485,810
4-Tour count T4 =176,869,111,720
Maximum degree dmax =28,837
Maximum left degree d1max =23,355
Maximum right degree d2max =28,837
Average degree d =37.506 2
Average left degree d1 =623.945
Average right degree d2 =19.334 2
Fill p =0.001 569 30
Average edge multiplicity m̃ =1.986 50
Size of LCC N =206,350
Diameter δ =6
50-Percentile effective diameter δ0.5 =3.453 54
90-Percentile effective diameter δ0.9 =3.890 98
Median distance δM =4
Mean distance δm =3.894 33
Gini coefficient G =0.938 415
Balanced inequality ratio P =0.074 570 9
Left balanced inequality ratio P1 =0.243 679
Right balanced inequality ratio P2 =0.107 938
Relative edge distribution entropy Her =0.789 169
Power law exponent γ =2.223 00
Tail power law exponent γt =1.641 00
Tail power law exponent with p γ3 =1.641 00
p-value p =0.000 00
Left tail power law exponent with p γ3,1 =1.691 00
Left p-value p1 =0.000 00
Right tail power law exponent with p γ3,2 =1.761 00
Right p-value p2 =0.000 00
Degree assortativity ρ =−0.157 788
Degree assortativity p-value pρ =0.000 00
Spectral norm α =3,181.60
Algebraic connectivity a =0.522 814
Spectral separation 1[A] / λ2[A]| =1.047 85
Controllability C =193,946
Relative controllability Cr =0.939 889

Plots

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

Delaunay graph drawing

Edge weight/multiplicity distribution

Temporal distribution

Diameter/density evolution

Inter-event distribution

Node-level inter-event distribution

Matrix decompositions plots

Downloads

References

[1] Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ]
[2] Julia Preusse. Analysis of the WebUni online student community. Master's thesis, Otto-von-Guericke-Universität Magdeburg, 2010.