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Mapping the Distribution and Spread of Social Ties Over Time: A Case Study Using Facebook Friends Cover

Mapping the Distribution and Spread of Social Ties Over Time: A Case Study Using Facebook Friends

Connections
Volume 39 (2019): Issue 1 (January 2019)
By: Clio Andris,  Sara E. Cavallo,  Elizabeth A. Dzwonczyk,  Laura Clemente-Harding,  Carolynne Hultquist and  Marie Ozanne  
Open Access
|Nov 2019

Figures & Tables

Figure 1.

Example force-directed social network visualizations. The large network represents all egos. The top right network represents the ego with the sparsest network, and at bottom right, the ego with the densest network. Visualizations created in the Gephi environment.
Example force-directed social network visualizations. The large network represents all egos. The top right network represents the ego with the sparsest network, and at bottom right, the ego with the densest network. Visualizations created in the Gephi environment.

Figure 2.

A collective distance decay distribution shows that in the pre-period (blue line), more alters lived nearby, and in the post-period (red line), more alters live between 1,500–3,000 km from the ego.
A collective distance decay distribution shows that in the pre-period (blue line), more alters lived nearby, and in the post-period (red line), more alters live between 1,500–3,000 km from the ego.

Figure 3.

An example map of one ego’s friends’ locations at inception (pre-cities) and at the time of the study (post-cities) shows the spreading of friend locations.
An example map of one ego’s friends’ locations at inception (pre-cities) and at the time of the study (post-cities) shows the spreading of friend locations.

Figure 4.

Individuals have fewer alters in red regions and more alters in blue regions over time, illustrating a shift toward coastal locales, North Carolina, and large cities in the West. Hot and cold spots are created using a kernel density function.
Individuals have fewer alters in red regions and more alters in blue regions over time, illustrating a shift toward coastal locales, North Carolina, and large cities in the West. Hot and cold spots are created using a kernel density function.

Figure A1.

Top cities for ego interaction at the time of friendship inception (as contours and white hot spots) are compared with the top cities for gravity model interaction (as hot and cold colors). Each number represents the rank of the city by its likelihood for interaction, given the gravity model’s prediction. White outlines represent popular cities for friends and yellow outlines represent cities with high theoretical interaction but few friends. Unpopular cities for friendships (marked in yellow) are peripheral to the location of the university.
Top cities for ego interaction at the time of friendship inception (as contours and white hot spots) are compared with the top cities for gravity model interaction (as hot and cold colors). Each number represents the rank of the city by its likelihood for interaction, given the gravity model’s prediction. White outlines represent popular cities for friends and yellow outlines represent cities with high theoretical interaction but few friends. Unpopular cities for friendships (marked in yellow) are peripheral to the location of the university.

Figure A2.

Top cities for ego interaction at the time of the study vis-à-vis top cities for gravity model interaction. Unpopular cities for friendships are still peripheral to the location of the university.
Top cities for ego interaction at the time of the study vis-à-vis top cities for gravity model interaction. Unpopular cities for friendships are still peripheral to the location of the university.

Table of group types and changes in standard distances_

Group typeModulesAvg. standard distance before; after (km)Difference in standard distance (km)Avg. years since inception
University362298; 381015128.5
Professional212468; 392714606.6
Secondary education20527; 2441191414.2
Place-based cultural group161437; 222478810.7
Family151678; 245978122.2
Non-residential gathering142898; 486519676.5
Non-place-based cultural group82463; 426718059.5
Other34429; 54109815.5

Top 20 most popular domestic alter cities and the number of egos with alters in these locations in the pre-period and post-period_

Pre-cityAltersEgosPost-cityAltersEgos
State College, PA138417State College, PA79718
Washington–Arlington–Alexandria, DC–VA–MD–WV80312Washington–Arlington–Alexandria, DC–VA–MD–WV72118
Seattle–Tacoma–Bellevue, WA3109New York–Northern New Jersey–Long Island, NY–NJ–PA34617
Poughkeepsie–Newburgh–Middletown, NY3063Boston–Cambridge–Quincy, MA–NH27617
Chicago–Naperville–Joliet, IL–IN–WI28810Charlotte–Gastonia–Concord, NC–SC2398
Boston–Cambridge–Quincy, MA–NH24513Seattle–Tacoma–Bellevue, WA23117
Knoxville, TN2225Chicago–Naperville–Joliet, IL–IN–WI22416
Cedar Rapids, IA2052Philadelphia–Camden–Wilmington, PA–NJ–DE–MD20715
Charlotte–Gastonia–Concord, NC–SC1994Atlanta–Sandy Springs–Marietta, GA19312
Atlanta–Sandy Springs–Marietta, GA1967San Francisco–Oakland–Fremont, CA18314
San Francisco–Oakland–Fremont, CA1868Los Angeles–Long Beach–Santa Ana, CA13317
Williamsport, PA1803Dallas–Fort Worth–Arlington, TX13215
Austin–Round Rock, TX1785Albany–Schenectady–Troy, NY1296
Philadelphia–Camden–Wilmington, PA–NJ–DE–MD1739Knoxville, TN1265
Dallas–Fort Worth–Arlington, TX1596Austin–Round Rock, TX12015
Appleton, WI1252Williamsport, PA1074
Riverside–San Bernardino–Ontario, CA1045Cedar Rapids, IA922
New York–Northern New Jersey–Long Island, NY–NJ–PA6316Pittsburgh, PA9115
Harrisburg–Carlisle, PA613Augusta–Richmond County, GA–SC825
San Diego–Carlsbad–San Marcos, CA457Riverside–San Bernardino–Ontario, CA767

Summary statistics and geographic distribution of alters for each ego’s network_

Network characteristicsGeographic characteristics
AgentNodesEdgesAverage degreeDiameterDensityClustering coefficientModules; modularityAverage distance to friends (post-period)Standard distance pre-periodStandard distance post-periodChange in standard distanceChange of mean center
A 376267912.3100.0380.364; 0.6143114682392924192
B 336253314.590.0450.5313; 0.67782755705691121667
C 601739223.5110.0410.4915; 0.6227715922395803383
D 370368619.490.0540.5711; 0.71256743773686-691597
E 573983333.8100.060.4712; 0.511409275437279731873
F 232270622.350.100.5312; 0.37117429173903986455
G 361298914.8120.040.5412; 0.7121588722831396368
H 437600227.280.060.4610; 0.5111856271267640188
I 403502224.780.0620.439; 0.5268730872383–704518
J 437276311.5100.0290.5213; 0.78161937264157431409
K 639978430.570.0480.3310; 0.4671225342893359575
L 188100210.4100.0570.646; 0.7469730472605–442129
M 2017846.9100.0390.1616; 0.46852257275610–117325
N 772714318.2110.0240.4416; 0.765293274335783292
O 123102815.890.1370.749; 0.406701358855641976448
P 65911,70834.980.0540.5014; 0.52150837263552–1741150
Q 72712,66734.590.0480.5310; 0.69492179229511159520
R 227441237.760.1720.5510; 0.231764916581609196
S 408763937.070.0920.529; 0.52626169329291236441
T 566415714.3140.0260.5315; 0.751148115621671011560
DOI: https://doi.org/10.21307/connections-2019-007 | Journal eISSN: 2816-4245 | Journal ISSN: 0226-1766
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Language: English
Page range: 1 - 17
Published on: Nov 14, 2019
Published by: International Network for Social Network Analysis (INSNA)
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Spatial social networks,
Geographic information systems,
Friend mapping,
Facebook,
Social ties
Related subjects:
Social sciences,
Social sciences, other

© 2019 Clio Andris, Sara E. Cavallo, Elizabeth A. Dzwonczyk, Laura Clemente-Harding, Carolynne Hultquist, Marie Ozanne, published by International Network for Social Network Analysis (INSNA)
This work is licensed under the Creative Commons Attribution 4.0 License.

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