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KONECT > Networks > Wikipedia talk, French

Wikipedia talk, French

About this network

This is the communication network of the French Wikipedia. Nodes represent users of the French Wikipedia, and an edge from user A to user B denotes that user A wrote a message on the talk page of user B at a certain timestamp.

Network info

CodeTfr
Category Communication
Data source https://zenodo.org/record/49561
Vertex type User
Edge type Message
FormatDirected: Edges are directed Directed
Edge weightsMultiple unweighted: Multiple edges are possible Multiple unweighted
Metadata Loops:  An edge may connect a node with itself LoopTimestamps:  Edges are annotated with a timestamps Timestamps
Size1,420,367 vertices (users)
Volume4,641,928 edges (messages)
Unique volume2,471,501 edges (messages)
Average degree (overall)6.5362 edges / vertex
Fill1.2251 10–6 edges / vertex2
Maximum degree1,096,752 edges
Reciprocity14.0%
Size of LCC1,409,540 vertices
Size of LSCC56,011 vertices
Wedge count623,970,181,277
Claw count2.2009822704717606 1017
Triangle count4,835,877
Square count11,749,091,667
4-tour count2,589,877,992,672
Power law exponent (estimated) with dmin2.6010 (dmin = 1)
Gini coefficient82.6%
Relative edge distribution entropy66.3%
Assortativity–0.35152
Clustering coefficient0.00233%
Diameter11 edges
90-percentile effective diameter3.54 edges
Mean shortest path length2.59 edges
Spectral norm1.1022 105
Algebraic connectivity0.024088
Preferential attachment exponent0.41061 (ε = 1.6696)
Temporal distribution of the Wikipedia talk, French network
Temporal distribution
Edge multiplicity distribution of the Wikipedia talk, French network
Edge multiplicity distribution
Cumulative edge multiplicity distribution of the Wikipedia talk, French network
Cumulative edge multiplicity distribution
Degree distribution of the Wikipedia talk, French network
Degree distribution
Outdegree distribution of the Wikipedia talk, French network
Outdegree distribution
Indegree distribution of the Wikipedia talk, French network
Indegree distribution
Degree distribution of the Wikipedia talk, French network
Degree distribution
Outdegree distribution of the Wikipedia talk, French network
Outdegree distribution
Indegree distribution of the Wikipedia talk, French network
Indegree distribution
Degree distribution of the Wikipedia talk, French network
Degree distribution
Outdegree distribution of the Wikipedia talk, French network
Outdegree distribution
Indegree distribution of the Wikipedia talk, French network
Indegree distribution
Clustering coefficient distribution of the Wikipedia talk, French network
Clustering coefficient distribution
Distance distribution of the Wikipedia talk, French network
Distance distribution
Distance distribution on a logistic scale of the Wikipedia talk, French network
Distance distribution on a logistic scale
Top-k eigenvalues of N of the Wikipedia talk, French network
Top-k eigenvalues of N
Top-k eigenvalues of L of the Wikipedia talk, French network
Top-k eigenvalues of L
Spectral distribution of the eigenvalues of A of the Wikipedia talk, French network
Spectral distribution of the eigenvalues of A
Spectral distribution of the eigenvalues of N of the Wikipedia talk, French network
Spectral distribution of the eigenvalues of N
Spectral distribution of the eigenvalues of L of the Wikipedia talk, French network
Spectral distribution of the eigenvalues of L
Cumulative spectral distribution of A of the Wikipedia talk, French network
Cumulative spectral distribution of A
Cumulative spectral distribution of N of the Wikipedia talk, French network
Cumulative spectral distribution of N
Cumulative spectral distribution of L of the Wikipedia talk, French network
Cumulative spectral distribution of L
Complex eigenvalues of the asymmetric adjacency matrix of the Wikipedia talk, French network
Complex eigenvalues of the asymmetric adjacency matrix

Downloads

TSV file:downloadwiki_talk_fr.tar.bz2 (25.56 MiB)

References

[1] Wikipedia talk, french network dataset -- KONECT, April 2017. [ http ]
[2] Jun Sun, Jérôme Kunegis, and Steffen Staab. Predicting user roles in social networks using transfer learning with feature transformation. In Proc. ICDM Workshop on Data Mining in Networks, 2016.

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