Twitter user–item

This is the bipartite network consisting of Twitter users and the URLs they mentioned in their postings. Left nodes represent users and right nodes represent URLs. An edge shows that an URL was mentioned by a user in a tweet.

Metadata

CodeWui
Internal namemunmun_twitterex_ui
NameTwitter user–item
Data sourcehttp://www.public.asu.edu/~mdechoud/datasets.html
AvailabilityDataset is not available for download
Consistency checkDataset passed all tests
Category
Interaction network
Dataset timestamp 2010
Node meaningUser, URL
Edge meaningMention
Network formatBipartite, undirected
Edge typeUnweighted, multiple edges
Temporal data Edges are annotated with timestamps
Snapshot Is a snapshot and likely to not contain all data

Statistics

Size n =9,374,206
Left size n1 =244,537
Right size n2 =9,129,669
Volume m =12,656,613
Unique edge count m̿ =10,214,177
Wedge count s =708,995,210
Claw count z =1,155,590,474,676
Cross count x =5,291,997,635,686,715
Square count q =53,711,135
4-Tour count T4 =3,286,104,890
Maximum degree dmax =24,106
Maximum left degree d1max =855
Maximum right degree d2max =24,106
Average degree d =2.700 31
Average left degree d1 =51.757 5
Average right degree d2 =1.386 32
Fill p =4.575 13 × 10−6
Average edge multiplicity m̃ =1.239 12
Size of LCC N =7,488,524
Diameter δ =30
50-Percentile effective diameter δ0.5 =7.524 28
90-Percentile effective diameter δ0.9 =9.761 17
Mean distance δm =8.134 08
Gini coefficient G =0.623 814
Balanced inequality ratio P =0.263 288
Left balanced inequality ratio P1 =0.285 079
Right balanced inequality ratio P2 =0.418 375
Relative edge distribution entropy Her =0.906 108
Power law exponent γ =8.945 62
Tail power law exponent γt =3.261 00
Degree assortativity ρ =−0.042 714 3
Degree assortativity p-value pρ =0.000 00
Spectral norm α =1,713.76

Plots

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

Edge weight/multiplicity distribution

Temporal distribution

Diameter/density evolution

Inter-event distribution

Node-level inter-event distribution

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] Munmun De Choudhury, Yu-Ru Lin, Hari Sundaram, K. Selçuk Candan, Lexing Xie, and Aisling Kelliher. How does the data sampling strategy impact the discovery of information diffusion in social media? In ICWSM, pages 34–41, 2010.