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KONECT > Networks > Wikipedia talk, Chinese

Wikipedia talk, Chinese

About this network

This is the communication network of the Chinese Wikipedia. Nodes represent users of the Chinese 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

CodeTzh
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
Size1,219,241 vertices (users)
Volume2,284,546 edges (messages)
Unique volume1,735,118 edges (messages)
Average degree (overall)3.7475 edges / vertex
Fill1.1672 10–6 edges / vertex2
Maximum degree937,210 edges
Reciprocity4.45%
Size of LCC1,217,365 vertices
Size of LSCC10,831 vertices
Wedge count454,139,500,995
Claw count1.3764640541547501 1017
Triangle count1,266,904
Square count6,675,967,773
4-tour count1,869,969,123,320
Power law exponent (estimated) with dmin2.8610 (dmin = 1)
Gini coefficient71.4%
Relative edge distribution entropy63.8%
Assortativity–0.42002
Clustering coefficient0.000837%
Diameter8 edges
90-percentile effective diameter3.72 edges
Mean shortest path length2.74 edges
Spectral norm4652.7
Algebraic connectivity0.037008
Preferential attachment exponent0.45024 (ε = 1.8742)
Temporal distribution of the Wikipedia talk, Chinese network
Temporal distribution
Edge multiplicity distribution of the Wikipedia talk, Chinese network
Edge multiplicity distribution
Cumulative edge multiplicity distribution of the Wikipedia talk, Chinese network
Cumulative edge multiplicity distribution
Degree distribution of the Wikipedia talk, Chinese network
Degree distribution
Outdegree distribution of the Wikipedia talk, Chinese network
Outdegree distribution
Indegree distribution of the Wikipedia talk, Chinese network
Indegree distribution
Degree distribution of the Wikipedia talk, Chinese network
Degree distribution
Outdegree distribution of the Wikipedia talk, Chinese network
Outdegree distribution
Indegree distribution of the Wikipedia talk, Chinese network
Indegree distribution
Degree distribution of the Wikipedia talk, Chinese network
Degree distribution
Outdegree distribution of the Wikipedia talk, Chinese network
Outdegree distribution
Indegree distribution of the Wikipedia talk, Chinese network
Indegree distribution
Clustering coefficient distribution of the Wikipedia talk, Chinese network
Clustering coefficient distribution
Distance distribution of the Wikipedia talk, Chinese network
Distance distribution
Distance distribution on a logistic scale of the Wikipedia talk, Chinese network
Distance distribution on a logistic scale
Top-k eigenvalues of A of the Wikipedia talk, Chinese network
Top-k eigenvalues of A
Top-k eigenvalues of N of the Wikipedia talk, Chinese network
Top-k eigenvalues of N
Top-k eigenvalues of L of the Wikipedia talk, Chinese network
Top-k eigenvalues of L
Spectral distribution of the eigenvalues of A of the Wikipedia talk, Chinese network
Spectral distribution of the eigenvalues of A
Spectral distribution of the eigenvalues of N of the Wikipedia talk, Chinese network
Spectral distribution of the eigenvalues of N
Spectral distribution of the eigenvalues of L of the Wikipedia talk, Chinese network
Spectral distribution of the eigenvalues of L
Cumulative spectral distribution of A of the Wikipedia talk, Chinese network
Cumulative spectral distribution of A
Cumulative spectral distribution of N of the Wikipedia talk, Chinese network
Cumulative spectral distribution of N
Cumulative spectral distribution of L of the Wikipedia talk, Chinese network
Cumulative spectral distribution of L
Complex eigenvalues of the asymmetric adjacency matrix of the Wikipedia talk, Chinese network
Complex eigenvalues of the asymmetric adjacency matrix

Downloads

TSV file:downloadwiki_talk_zh.tar.bz2 (12.01 MiB)

References

[1] Wikipedia talk, chinese 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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