Adolescent health
This directed network was created from a survey that took place in 1994/1995.
Each student was asked to list his 5 best female and his 5 male friends. A node
represents a student and an edge between two students shows that the left
student chose the right student as a friend. Higher edge weights indicate more
interactions and a edge weight shows that there is no common activity at all.
Metadata
Statistics
Size  n =  2,539

Volume  m =  12,969

Loop count  l =  0

Wedge count  s =  99,247

Claw count  z =  740,457

Cross count  x =  2,920,116

Triangle count  t =  4,694

Square count  q =  17,977

4Tour count  T_{4} =  561,714

Maximum degree  d_{max} =  36

Maximum outdegree  d^{+}_{max} =  10

Maximum indegree  d^{−}_{max} =  27

Average degree  d =  10.215 8

Fill  p =  0.002 012 58

Size of LCC  N =  2,539

Size of LSCC  N_{s} =  2,155

Relative size of LSCC  N^{r}_{s} =  0.848 759

Diameter  δ =  10

50Percentile effective diameter  δ_{0.5} =  4.064 73

90Percentile effective diameter  δ_{0.9} =  5.304 82

Median distance  δ_{M} =  5

Mean distance  δ_{m} =  4.516 47

Gini coefficient  G =  0.299 855

Relative edge distribution entropy  H_{er} =  0.981 136

Power law exponent  γ =  1.514 12

Tail power law exponent  γ_{t} =  8.251 00

Degree assortativity  ρ =  +0.251 286

Degree assortativity pvalue  p_{ρ} =  1.605 64 × 10^{−298}

In/outdegree correlation  ρ^{±} =  +0.260 294

Clustering coefficient  c =  0.141 888

Directed clustering coefficient  c^{±} =  0.149 544

Spectral norm  α =  59.167 9

Operator 2norm  ν =  31.946 0

Cyclic eigenvalue  π =  27.584 2

Algebraic connectivity  a =  0.510 714

Reciprocity  y =  0.387 694

Nonbipartivity  b_{A} =  0.548 498

Normalized nonbipartivity  b_{N} =  0.216 902

Algebraic nonbipartivity  χ =  0.319 993

Spectral bipartite frustration  b_{K} =  0.009 713 80

Plots
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]

James Moody.
Peer influence groups: Identifying dense clusters in large networks.
Soc. Netw., 23(4):261–283, 2001.
