This is a network of co-authorships in the area of network science.
Code | NS
| |
Internal name | dimacs10-netscience
| |
Name | Network science | |
Data source | https://www.cc.gatech.edu/dimacs10/archive/clustering.shtml | |
Availability | Dataset is available for download | |
Consistency check | Dataset passed all tests | |
Category | Co-authorship network | |
Dataset timestamp | 2006 | |
Node meaning | Author | |
Edge meaning | Co-authorship | |
Network format | Unipartite, undirected | |
Edge type | Unweighted, no multiple edges | |
Loops | Does not contain loops | |
Join | Is the join of an underlying network | |
Multiplicity | Does not have multiple edges, but the underlying data has |
Size | n = | 1,461 |
Volume | m = | 2,742 |
Loop count | l = | 0 |
Wedge count | s = | 16,284 |
Claw count | z = | 57,925 |
Cross count | x = | 221,122 |
Triangle count | t = | 3,764 |
Square count | q = | 22,787 |
4-Tour count | T_{4} = | 252,916 |
Maximum degree | d_{max} = | 34 |
Average degree | d = | 3.753 59 |
Fill | p = | 0.002 570 95 |
Size of LCC | N = | 379 |
Diameter | δ = | 17 |
50-Percentile effective diameter | δ_{0.5} = | 5.642 87 |
90-Percentile effective diameter | δ_{0.9} = | 9.182 06 |
Median distance | δ_{M} = | 6 |
Mean distance | δ_{m} = | 6.285 28 |
Gini coefficient | G = | 0.415 264 |
Relative edge distribution entropy | H_{er} = | 0.957 949 |
Power law exponent | γ = | 1.970 78 |
Tail power law exponent | γ_{t} = | 3.611 00 |
Degree assortativity | ρ = | +0.461 622 |
Degree assortativity p-value | p_{ρ} = | 1.231 55 × 10^{−287} |
Clustering coefficient | c = | 0.693 441 |
Spectral norm | α = | 19.023 8 |
Algebraic connectivity | a = | 0.015 203 8 |
Non-bipartivity | b_{A} = | 0.712 488 |
Normalized non-bipartivity | b_{N} = | 0.255 201 |
Algebraic non-bipartivity | χ = | 0.462 123 |
Spectral bipartite frustration | b_{K} = | 0.023 953 0 |
[1] | Jérôme Kunegis. KONECT – The Koblenz Network Collection. In Proc. Int. Conf. on World Wide Web Companion, pages 1343–1350, 2013. [ http ] |
[2] | Mark E. J. Newman. Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E, 74(3), 2006. |