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

Clio Andris
Clio Andris
, 
Sara E. Cavallo
Sara E. Cavallo
, 
Elizabeth A. Dzwonczyk
Elizabeth A. Dzwonczyk
, 
Laura Clemente-Harding
Laura Clemente-Harding
, 
Carolynne Hultquist
Carolynne Hultquist
 e 
Marie Ozanne
Marie Ozanne
   | 14 nov 2019
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Pubblicato online: 14 nov 2019
Pagine: 1 - 17
DOI: https://doi.org/10.21307/connections-2019-007
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© 2019 Clio Andris et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

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 type Modules Avg. standard distance before; after (km) Difference in standard distance (km) Avg. years since inception
University 36 2298; 3810 1512 8.5
Professional 21 2468; 3927 1460 6.6
Secondary education 20 527; 2441 1914 14.2
Place-based cultural group 16 1437; 2224 788 10.7
Family 15 1678; 2459 781 22.2
Non-residential gathering 14 2898; 4865 1967 6.5
Non-place-based cultural group 8 2463; 4267 1805 9.5
Other 3 4429; 5410 981 5.5

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-city Alters Egos Post-city Alters Egos
State College, PA 1384 17 State College, PA 797 18
Washington–Arlington–Alexandria, DC–VA–MD–WV 803 12 Washington–Arlington–Alexandria, DC–VA–MD–WV 721 18
Seattle–Tacoma–Bellevue, WA 310 9 New York–Northern New Jersey–Long Island, NY–NJ–PA 346 17
Poughkeepsie–Newburgh–Middletown, NY 306 3 Boston–Cambridge–Quincy, MA–NH 276 17
Chicago–Naperville–Joliet, IL–IN–WI 288 10 Charlotte–Gastonia–Concord, NC–SC 239 8
Boston–Cambridge–Quincy, MA–NH 245 13 Seattle–Tacoma–Bellevue, WA 231 17
Knoxville, TN 222 5 Chicago–Naperville–Joliet, IL–IN–WI 224 16
Cedar Rapids, IA 205 2 Philadelphia–Camden–Wilmington, PA–NJ–DE–MD 207 15
Charlotte–Gastonia–Concord, NC–SC 199 4 Atlanta–Sandy Springs–Marietta, GA 193 12
Atlanta–Sandy Springs–Marietta, GA 196 7 San Francisco–Oakland–Fremont, CA 183 14
San Francisco–Oakland–Fremont, CA 186 8 Los Angeles–Long Beach–Santa Ana, CA 133 17
Williamsport, PA 180 3 Dallas–Fort Worth–Arlington, TX 132 15
Austin–Round Rock, TX 178 5 Albany–Schenectady–Troy, NY 129 6
Philadelphia–Camden–Wilmington, PA–NJ–DE–MD 173 9 Knoxville, TN 126 5
Dallas–Fort Worth–Arlington, TX 159 6 Austin–Round Rock, TX 120 15
Appleton, WI 125 2 Williamsport, PA 107 4
Riverside–San Bernardino–Ontario, CA 104 5 Cedar Rapids, IA 92 2
New York–Northern New Jersey–Long Island, NY–NJ–PA 63 16 Pittsburgh, PA 91 15
Harrisburg–Carlisle, PA 61 3 Augusta–Richmond County, GA–SC 82 5
San Diego–Carlsbad–San Marcos, CA 45 7 Riverside–San Bernardino–Ontario, CA 76 7

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

Network characteristics Geographic characteristics
Agent Nodes Edges Average degree Diameter Density Clustering coefficient Modules; modularity Average distance to friends (post-period) Standard distance pre-period Standard distance post-period Change in standard distance Change of mean center
A 376 2679 12.3 10 0.038 0.36 4; 0.61 431 1468 2392 924 192
B 336 2533 14.5 9 0.045 0.53 13; 0.67 7827 5570 5691 121 667
C 601 7392 23.5 11 0.041 0.49 15; 0.62 277 1592 2395 803 383
D 370 3686 19.4 9 0.054 0.57 11; 0.71 2567 4377 3686 -691 597
E 573 9833 33.8 10 0.06 0.47 12; 0.51 1409 2754 3727 973 1873
F 232 2706 22.3 5 0.10 0.53 12; 0.37 1174 2917 3903 986 455
G 361 2989 14.8 12 0.04 0.54 12; 0.71 215 887 2283 1396 368
H 437 6002 27.2 8 0.06 0.46 10; 0.51 1185 627 1267 640 188
I 403 5022 24.7 8 0.062 0.43 9; 0.52 687 3087 2383 –704 518
J 437 2763 11.5 10 0.029 0.52 13; 0.78 1619 3726 4157 431 409
K 639 9784 30.5 7 0.048 0.33 10; 0.46 712 2534 2893 359 575
L 188 1002 10.4 10 0.057 0.64 6; 0.74 697 3047 2605 –442 129
M 201 784 6.9 10 0.039 0.16 16; 0.46 8522 5727 5610 –117 325
N 772 7143 18.2 11 0.024 0.44 16; 0.76 529 3274 3357 83 292
O 123 1028 15.8 9 0.137 0.74 9; 0.40 6701 3588 5564 1976 448
P 659 11,708 34.9 8 0.054 0.50 14; 0.52 1508 3726 3552 –174 1150
Q 727 12,667 34.5 9 0.048 0.53 10; 0.69 492 1792 2951 1159 520
R 227 4412 37.7 6 0.172 0.55 10; 0.23 176 49 1658 1609 196
S 408 7639 37.0 7 0.092 0.52 9; 0.52 626 1693 2929 1236 441
T 566 4157 14.3 14 0.026 0.53 15; 0.75 1148 1156 2167 1011 560
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