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

Code		`Wui`
Internal name		`munmun_twitterex_ui`
Name		Twitter user–item
Data source		http://www.public.asu.edu/~mdechoud/datasets.html
Availability		Dataset is not available for download
Consistency check		Dataset passed all tests
Category		Interaction network
Dataset timestamp		2010
Node meaning		User, URL
Edge meaning		Mention
Network format		Bipartite, undirected
Edge type		Unweighted, 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	d_max =	24,106
Maximum left degree	d_1max =	855
Maximum right degree	d_2max =	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	δ_0.5 =	7.524 28
90-Percentile effective diameter	δ_0.9 =	9.761 17
Median distance	δ_M =	8
Mean distance	δ_m =	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	H_er =	0.906 108
Power law exponent	γ =	8.945 62
Tail power law exponent	γ_t =	3.261 00
Degree assortativity	ρ =	−0.042 714 3
Degree assortativity p-value	p_ρ =	0.000 00
Spectral norm	α =	1,713.76
Spectral separation	\|λ₁[A] / λ₂[A]\| =	1.750 05
Controllability	C =	8,893,258
Relative controllability	C_r =	0.948 695

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

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

Downloads

Original dataset source
KONECT extraction code (GitHub)

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.