gelada_baboons — Gelada baboon grooming (1975)

Description

Grooming relations in a colony of gelada baboons (unit H22), from the field study of R. Dunbar and P. Dunbar in Ethiopia, as analyzed by K. Stephenson and M. Zelen. The network was observed at two time points: at the first there were 12 baboons (seven females and five males), and before the second two more baboons (a female and a male) were introduced to the group and accepted. Nodes are baboons, and an undirected edge connects two baboons that were observed grooming each other, weighted by the frequency of grooming. The data were distributed on the Pajek datasets page of the book Generalized Blockmodeling (Doreian, Batagelj and Ferligoj), from which this version was obtained. The binarized versions of the networks also distributed there are not included.

t1: the 12 baboons at the first time point (H22A).
t2: the 14 baboons at the second time point (H22B).

The networks have the following properties:

Nodes: name, a label encoding the sex (F: female, M: male), age class ("adult", or the age in years) and role (D: dominant, L: leader, F: follower, N: newcomer) of the baboon; code, a short identifier (f1 to f8 for females, m1 to m6 for males) used in the book Generalized Blockmodeling; female, whether the baboon is female.
Edges: weight, the frequency of grooming.

Tags

Social Animal Weighted Metadata

Citation

R. Dunbar, P. Dunbar, "Social Dynamics of Gelada Baboons." Contributions to Primatology 6, 1-157 (1975) web.archive.org
K. Stephenson, M. Zelen, "Rethinking centrality: Methods and examples." Social Networks 11(1), 1-37 (1989) 10.1016/0378-8733(89)90016-6 Sci-Hub
P. Doreian, V. Batagelj, A. Ferligoj, "Generalized Blockmodeling." Cambridge University Press (2004) 10.1017/CBO9780511584176 Sci-Hub

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https://web.archive.org/web/20130507093317/http://vlado.fmf.uni-lj.si/pub/networks/data/GBM/baboons.htm

Networks

Tip: click on a column header to sort the list, or hover over it for a description.

Column legend

$\left<k\right>$: Mean degree
$\sigma_k$: Standard deviation of the degrees
$\lambda_h$: Largest eigenvalue of the Hashimoto (non-backtracking) matrix
$\tau$: Random walk mixing time
$r$: Degree assortativity
$c$: Global clustering coefficient
$\oslash$: (Pseudo-) diameter
$S$: Relative size of the largest component
NPs: Node properties
EPs: Edge properties

Name	Nodes	Edges	$\left<k\right>$	$\sigma_k$	$\lambda_h$	$\tau$	$r$	$c$	$\oslash$	$S$	Kind	Mode	NPs	EPs	gt	GraphML	GML	csv
t1	12	24	4.00	1.91	3.79	2.06	-0.17	0.41	4	1.00	Undirected	Unipartite	name code female	weight	1 KiB	1 KiB	1 KiB	1 KiB
t2	14	23	3.29	1.71	2.80	3.82	-0.58	0.29	4	1.00	Undirected	Unipartite	name code female	weight	1 KiB	1 KiB	1 KiB	1 KiB