us_county_fb — US counties linked by Facebook social connectedness

Description

Networks of counties of the United States linked by the Social Connectedness Index of Facebook (the relative probability that two users living in the two counties are friends on Facebook):

strong_ties: the network compiled by J. Jia and A. R. Benson as a benchmark for graph-based regression of election outcomes, with demographic and election features of the counties.
sci: the complete, unthresholded county-to-county Social Connectedness Index as published by Meta (Data for Good) on the Humanitarian Data Exchange (HDX), weighted by the index.

In strong_ties, nodes are counties (identified by their FIPS codes), and an undirected edge connects two counties with strong social ties according to the Social Connectedness Index of Facebook (the relative probability that two users living in the two counties are friends on Facebook): each county is connected to those it is most strongly tied to, about 20 per county according to the authors. The network is not planar, since strong ties also exist between distant counties. The edge list of the data contains 584 pairs of counties twice, once in each direction; they are merged into one undirected edge. The raw values of the Social Connectedness Index are not included in this network.

The counties of strong_ties have demographic features from the US Department of Agriculture and the results of the presidential elections of 2012 and 2016 (formatted by T. McGovern), as well as features of the Facebook friendships. All features are normalized by the authors to zero mean and unit standard deviation over the counties; the raw values are not given.

The sci network contains all 3,204 counties and county equivalents of the file (including those of Puerto Rico and the US Virgin Islands) and an edge for every pair of them, weighted by the scaled index (from 1 to 1,000,000,000), including a self-loop for each county (the connectedness of its users among themselves). The file gives each pair in both directions, with the same value; each pair is kept once. It is a later release of the index (built from a snapshot of Facebook friendships of December 2025 to January 2026, with a revised methodology) than the one used for strong_ties, which Meta no longer provides: older releases are not published, as the index is not meant to be compared over time. HDX replaces the file in place with new releases; the version of May 11, 2026 is used.

The strong_ties network has the following properties:

(Node) fips: the FIPS code of the county.
(Node) sh050m, sh100m, sh500m: the share of the Facebook friends of the county's users who live within 50, 100 and 500 miles (normalized).
(Node) income_<year>, migration_<year>, birth_<year>, death_<year>, education_<year>, unemployment_<year> (for the years 2012 and 2016): the median household income, the net migration rate, the birth and death rates, the education level and the unemployment rate of the county (normalized).
(Node) election_<year>: the margin of the presidential election in the county, (Republican votes - Democratic votes) / (Republican votes + Democratic votes) (normalized).

The sci network has the following properties:

(Node) fips: the FIPS code of the county.
(Edge) weight: the scaled Social Connectedness Index of the two counties.

Tags

Social Online Political Unweighted Weighted Metadata

Added

2026-10-27

Citation

J. Jia and A. R. Benson, "A Unifying Generative Model for Graph Learning Algorithms: Label Propagation, Graph Convolutions, and Combinations." SIAM Journal on Mathematics of Data Science 4(1), 100-125 (2022). 10.1137/21M1395351 Sci-Hub
M. Bailey, R. Cao, T. Kuchler, J. Stroebel, A. Wong, "Social Connectedness: Measurement, Determinants, and Effects." Journal of Economic Perspectives 32(3), 259-280 (2018). 10.1257/jep.32.3.259 Sci-Hub

Upstream URL OK

https://www.cs.cornell.edu/~arb/data/US-county-fb/
https://data.humdata.org/dataset/social-connectedness-index

Upstream license

CC0 (sci); none stated (strong_ties), https://creativecommons.org/publicdomain/zero/1.0/

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
strong_ties	3,106	22,574	14.54	5.03	17.59	162.63	-0.02	0.45	12	1.00	Undirected	Unipartite	fips sh050m sh100m sh500m income_2012 migration_2012 birth_2012 death_2012 education_2012 unemployment_2012 election_2012 income_2016 migration_2016 birth_2016 death_2016 education_2016 unemployment_2016 election_2016		244 KiB	319 KiB	310 KiB	306 KiB
sci	3,204	5,134,410	3205.00	0.00	3204.00	0.12	nan	1.00	1	1.00	Undirected	Unipartite	fips	weight	17.9 MiB	29.0 MiB	28.0 MiB	24.6 MiB