endow_sites — ENDOW project community networks

Description

Social networks of rural communities studied in the ENDOW project ("Evolutionary and Dynamic Origins of Wealth"), a cross-cultural project that collected comparable data on kinship, cooperation, social support and wealth in more than 20 communities around the world (funded by the US National Science Foundation, award 1743019). The project's own repositories contain only simulated ("faux") data; the networks here are those that the authors of the studies that use ENDOW data (listed on the project's publications page) released openly, one network per community:

mosuo_labai and mosuo_yongning: two communities of Mosuo agriculturalists in Yunnan, China, the patrilineal Labai and the matrilineal Yongning (Mattison et al. 2021). Nodes are individuals, and edges are the nominations of the respondents (one per household) for the questions of the survey (the layers): who they borrow money from and lend it to, borrow farm equipment from and lend it to, who helps with the harvest, home repairs, slaughtering, childcare and the preparation of community meals, who pays school fees, with whom the women and the men of the household spend time after dinner, kin relations (Relations), influential people outside the community, government and NGO contacts, and who they ride with. Some questions were answered by the respondent on behalf of other household members (e.g. the men's and women's evening company); the edges are as recorded, from the respondent. Two identifiers are corrected as in the authors' code (YN9076 to YN2000, and YN2016 to YN2017).
pemba_mitini and pemba_mitini_sharing: the village of Mitini, on the island of Pemba, Zanzibar (Redhead et al. 2023). In pemba_mitini, the nodes are the households (pesu...), and the layers work_women and work_men (with whom the women and the men commonly work) and friends_women and friends_men (with whom they spend free time) go from the nominating household to the nominated one. In pemba_mitini_sharing, the nodes are the "sharing units" of the food sharing data (pessu...; some of them merge two households, e.g. pessu101108 for households 101 and 108), with the layers food_help_received (which households commonly help you with food) and food_given (to which households do you often give food), from the nominating unit to the nominated one, and relatedness, the mean genetic relatedness between the units (undirected, given as weighted edges in one direction); the household attributes are given for the units that are single households. The two networks are kept apart because their nodes differ.
matlab_bangladesh: the households of a village in Matlab, Bangladesh (Hwang et al. 2026). Layers: financial (cash), material and labor support, as recorded (the authors note that the direction of the labor edges is the reverse of the flow of the help), and kinship between households.
shodagor: Shodagor women (a community of semi-nomadic traders and fishers in Bangladesh) and their partners in childcare and work, as recorded (Starkweather et al. 2022), with relatedness (weighted) edges.
colombia: friendship nominations in rural Colombia (Afrocolombian, Emberá and Mestizo individuals), from the data published with the STRAND package by C. T. Ross (Colombia_Data, a sample of 149 individuals of the study of Redhead, Dalla Ragione and Ross 2023), with relatedness (weighted) edges.

Several other studies on the publications page were checked: their network data are not openly available (only on request, e.g. the South Indian Tamil data of E. A. Power, the fishing communities of Barnes et al. 2020; or not released), or the studies do not concern networks. Physical distances between households, given as matrices, are not included.

The networks have the following properties:

(Node) name: the identifier of the individual or household in the data.
(Edge) layer: the question or relation of the edge (see above).
(Edge) weight: the relatedness for relatedness edges, and 1 for the others.
(Node) Other properties, depending on the community, as given in the data:
(Node) household, relationship_to_head, birth_year, age, gender, marital_status, years_of_education, occupation, group, status, wealth, ability_to_work, location (Mosuo): as given in the data.
(Node) wives (Pemba): number of wives of the household in 2018.
(Node) wealth (Pemba): direct material wealth.
(Node) log_wealth (Pemba): as given in the data.
(Node) status (Pemba): a member holds a position.
(Node) khazi (Pemba): a member belongs to the labor cooperative.
(Node) household_size, head_age, status, salaried, land, wealth_usd (Matlab): as given in the data.
(Node) household (Shodagor): as given in the data.
(Node) ethnicity, sex, age, bmi, pa (Colombia): as given in the data.

Tags

Social Offline Multilayer Multigraph Metadata

Added

2026-10-31

Citation

S. M. Mattison, N. G. MacLaren, R. Liu, A. Z. Reynolds, G. D. Baca, P. M. Mattison, M. Zhang, C.-Y. Sum, M. K. Shenk, T. Blumenfield, C. von Rueden and K. Wander, "Gender Differences in Social Networks Based on Prevailing Kinship Norms in the Mosuo of China." Social Sciences 10(7), 253 (2021) 10.3390/socsci10070253 Sci-Hub
D. Redhead, E. Maliti, J. B. Andrews and M. Borgerhoff Mulder, "The interdependence of relational and material wealth inequality in Pemba, Zanzibar." Phil. Trans. R. Soc. B 378, 20220288 (2023) 10.1098/rstb.2022.0288 Sci-Hub
J. Hwang, N. G. MacLaren, D. Nolin, S. Cully, N. Alam and M. K. Shenk, "Reciprocal exchanges across multilayered networks show an emerging patron-client system led by salaried households." Evolutionary Human Sciences 8, e29 (2026) 10.1017/ehs.2026.10061 Sci-Hub
K. E. Starkweather, A. Z. Reynolds, F. Zohora and N. Alam, "Shodagor women traders cooperate across domains of work and childcare to solve an adaptive problem." Phil. Trans. R. Soc. B 378, 20210433 (2022) 10.1098/rstb.2021.0433 Sci-Hub
D. Redhead, A. Dalla Ragione and C. T. Ross, "Friendship and partner choice in rural Colombia." Evolution and Human Behavior 44(5), 430-441 (2023) 10.1016/j.evolhumbehav.2022.08.004 Sci-Hub

Upstream URL OK

https://endowproject.github.io/publications/
https://github.com/smattison/Social-Sciences-Data
https://github.com/DanielRedhead/Pemba-wealth
https://github.com/JoonHwang-psu/MatlabMultinetwork
https://github.com/kstarkweather8/WomensNetworks_2022
https://github.com/ctross/STRAND

Upstream license

Mosuo: MIT; Colombia (STRAND): BSD-3-Clause; others: none given, https://endowproject.github.io/publications/

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
mosuo_labai	274	909	3.32	12.58	8.91	13.99	-0.26	0.17	7	0.73	Directed	Unipartite	name ability_to_work age birth_year gender group household location marital_status occupation relationship_to_head status wealth years_of_education	layer weight	12 KiB	17 KiB	16 KiB	14 KiB
mosuo_yongning	347	517	1.49	5.63	6.32	14.01	-0.20	0.10	7	0.66	Directed	Unipartite	name ability_to_work age birth_year gender group household location marital_status occupation relationship_to_head status wealth years_of_education	layer weight	14 KiB	18 KiB	17 KiB	15 KiB
pemba_mitini	44	697	15.84	14.19	18.48	1.91	0.13	0.58	3	1.00	Directed	Unipartite	name khazi log_wealth status wealth wives	layer weight	5 KiB	7 KiB	7 KiB	6 KiB
pemba_mitini_sharing	38	577	15.18	13.59	13.81	3.27	0.10	0.57	3	1.00	Directed	Unipartite	name khazi log_wealth status wealth wives	layer weight	5 KiB	7 KiB	6 KiB	6 KiB
matlab_bangladesh	79	1,176	14.89	13.87	10.25	7.40	0.13	0.31	5	1.00	Directed	Unipartite	name head_age household_size land salaried status wealth_usd	layer weight	6 KiB	10 KiB	9 KiB	7 KiB
shodagor	83	260	3.13	4.83	9.83	19.48	0.57	0.60	7	0.76	Directed	Unipartite	name household	layer weight	5 KiB	7 KiB	6 KiB	6 KiB
colombia	149	407	2.73	4.35	7.69	51.89	0.27	0.37	10	0.93	Directed	Unipartite	name ethnicity sex age bmi pa	layer weight	8 KiB	11 KiB	10 KiB	9 KiB

Ridiculograms^*

pemba_mitini_sharing drawing — pemba_mitini_sharing

matlab_bangladesh drawing — matlab_bangladesh

^* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.