Baidu internal

This is the directed network of hyperlinks between the articles of the Chinese online encyclopedia Baidu (百度百科, http://baike.baidu.com/).

Metadata

CodeBAi
Internal namezhishi-baidu-internallink
NameBaidu internal
Data sourcehttp://zhishi.me/
AvailabilityDataset is available for download
Consistency checkDataset passed all tests
Category
Hyperlink network
Node meaningArticle
Network formatUnipartite, directed
Edge typeUnweighted, multiple edges
ReciprocalContains reciprocal edges
Directed cyclesContains directed cycles
LoopsContains loops

Statistics

Size n =2,141,300
Volume m =17,794,839
Unique edge count m̿ =17,643,697
Loop count l =11,507
Wedge count s =30,884,847,288
Claw count z =574,625,756,279,109
Cross count x =1.202 65 × 1019
Triangle count t =25,207,196
Maximum degree dmax =97,950
Maximum outdegree d+max =2,596
Maximum indegree dmax =97,950
Average degree d =16.620 6
Fill p =3.848 00 × 10−6
Average edge multiplicity m̃ =1.008 57
Size of LCC N =2,107,689
Size of LSCC Ns =609,905
Relative size of LSCC Nrs =0.284 829
Diameter δ =20
50-Percentile effective diameter δ0.5 =3.599 88
90-Percentile effective diameter δ0.9 =4.873 05
Mean distance δm =4.175 31
Gini coefficient G =0.775 692
Balanced inequality ratio P =0.187 371
Outdegree balanced inequality ratio P+ =0.227 681
Indegree balanced inequality ratio P =0.157 618
Relative edge distribution entropy Her =0.880 719
Power law exponent γ =1.672 78
Tail power law exponent γt =2.291 00
Degree assortativity ρ =−0.031 373 3
Degree assortativity p-value pρ =0.000 00
Clustering coefficient c =0.002 448 50
Directed clustering coefficient c± =0.020 564 0
Spectral norm α =485.196
Operator 2-norm ν =442.626
Cyclic eigenvalue π =88.793 0
Reciprocity y =0.070 619 8
Non-bipartivity bA =0.090 247 1
Normalized non-bipartivity bN =0.003 126 37
Spectral bipartite frustration bK =9.639 83 × 10−5

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 normalized adjacency matrix

Hop distribution

In/outdegree scatter plot

Edge weight/multiplicity distribution

Clustering coefficient 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] Xing Niu, Xinruo Sun, Haofen Wang, Shu Rong, Guilin Qi, and Yong Yu. Zhishi.me – weaving Chinese linking open data. In Proc. Int. Semant. Web Conf., pages 205–220, 2011.