Read: Social network analysis - theory and application
참조: Introduction to social network methods
SA, introduction to sna in Models for Social Networks With Statistical Applications (Advanced Quantitative Techniques in the Social Sciences series) 1412941687
Crime and Social Network Analysis
social studies of people . . . studies of attributes of people = attribute studies
Gender | Race | Height | Income | Marital Status | Years of Educ | Liberal- ness |
|
---|---|---|---|---|---|---|---|
p0001 | M | 1 | 170 | 5000 | 1 | 12 | 1.73 |
p0002 | M | 2 | 183 | 10000 | 1 | 20 | 4.53 |
p0003 | F | 1 | 160 | 2500 | 1 | 16 | 2.99 |
p0004 | M | 2 | 175 | 3000 | 2 | 16 | 1.13 |
p0005 | F | 1 | 159 | 3500 | 3 | 12 | 3.81 |
p0006 | M | 1 | 186 | 4500 | 1 | 9 | 4.76 |
p0007 | F | 5 | 162 | 5000 | 2 | 21 | 2.01 |
p0008 | F | 4 | 154 | 1200 | 1 | 18 | 1.27 |
p0009 | M | 1 | 172 | 5300 | 1 | 12 | 3.25 |
일반적으로 SNA는 아래의 것들을 강조한다.
또 다른 approach로 아래를 생각해 볼 수 있다. 아래는 미국 주요 도시 간의 거리를 나타내는 테이블 형식의 데이터이다. 역기서 중요한 것은 각 도시 간의 거리를 알고있다면 (정확하다고 하면), 연구자는 도시들 간의 거리만을 표시함으로써 실제 지형의 맵을 완성할 수 있다는 것이다.
Cities | Boston | Chicago | Denver | LosAngeles | New York | San Francisco | Seattle | Washington |
---|---|---|---|---|---|---|---|---|
Boston, Mass. | - | 851 | 1769 | 2596 | 188 | 2699 | 2493 | 393 |
Chicago, Ill. | 851 | - | 920 | 1745 | 713 | 1858 | 1737 | 597 |
Denver, Colo. | 1769 | 920 | - | 831 | 1631 | 949 | 1021 | 1494 |
Los Angeles, Calif. | 2596 | 1745 | 831 | - | 2451 | 347 | 959 | 2300 |
New York, N.Y. | 188 | 713 | 1631 | 2451 | - | 2571 | 2408 | 205 |
San Francisco, Calif. | 2699 | 1858 | 949 | 347 | 2571 | - | 678 | 2442 |
Seattle, Wash. | 2493 | 1737 | 1021 | 959 | 2408 | 678 | - | 2329 |
Washington, D.C. | 393 | 597 | 1494 | 2300 | 205 | 2442 | 2329 | - |
아래는 위의 데이터에 기반하여 만든 맵이다. 이 맵은 이제, 실제 지도와 흡사한 모양을 가지게 된다.
이를 사람의 관계에 적용한다고 가정하면, 그리고 그 관계의 거리를 정확하게 측정할 수 있는 방법만 있다면, 그 사회의 사회적인 맵을 완성할 수도 있다고 결론지을 수도 있다. 이것이 관계의 그래프이론이다. 그리고, 이런 류의 이론은 측정(metric)을 중요시하고 이를 위해서 많은 노력을 기울인다 1).
이런 관계의 측정을 연구하면서 사회관계망 연구자들의 사람들 간의 수치화된 관계 외에 관계의 있고/없음을 (binary) 기록한 데이터로서 그 사회의 일 단면을 살펴보는 단서가 된다고 발전시켰다. 또한 이에 더 나아가, 사건이나 상황에 대한 참여(participation), 관여(involvement), 등 또한 관계망 연구에 활용이 된다고 하였다.
Examples
Terms
Study of (see Measures in social network analysis)
Node measurements
Nodes . . . what could they be?
Who is more influential in this type of social relationship arrangement?
Matrix calculation
see How to Multiply Matrices at maths is fun site.
In R . . . .
matrix (c(1,1,1,2…)), byrow=T, nrow=36, ncol=2)
이 의미하는 것은 ncol=2 이므로
1,1
1,2
1,3
과 같은 데이터형식을 같는다는 것(nrow와 ncol에 의해서)과
1과1, 1과2 등은 관계가 있음을 나타내 주는 것이다.
classtaken = matrix(0,8,10) # 0으로 채워진 8 x 10 크기의 matrix 만들기 # 8 students # 10 classes classtaken edge.list = matrix ( c(1,1,1,2,1,3,1,4,1,9, 2,2,2,5,2,7,2,8, 3,1,3,5,3,6,3,7,3,8, 4,2,4,6,4,9,4,10, 5,1,5,2,5,5,5,7,5,8, 6,2,6,3,6,4,6,7, 7,3,7,4,7,7,7,8, 8,1,8,2,8,6,8,9,8,10), byrow=T, nrow=36,ncol=2) # 둘로 짝지어진 (ncol = 2) 관계 위치를 edge.list에 기록하기 # (1,1)은 매트릭스의 row 1, column 1 위치를 의미 classtaken[edge.list] = 1 # 0으로만 채워졌던 classtaken matrix에 [edge.list]자리는 0을 1로 바꾸기 # 위에서 언급된 위치에 1을 주기 classtaken # classtaken 데이터 확인 rownames(classtaken) = c("a","b", "c", "d","e", "f", "g", "h") colnames(classtaken) = c("writer", "comtheo", "pr","adv", "broadc","internet","camshoot", "edit", "newmedia", "cmc") classtaken c = classtaken tc = t(classtaken) stu = c %*% tc class = tc %*% c stu class
Two mode matrix (students x classes taken) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
ID | writer | comTheo | pr | adv | broadc | internet | camShoot | edit | newMedia | CMC |
a | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
b | 0 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
c | 1 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 |
d | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 |
e | 1 | 1 | 0 | 0 | 1 | 0 | 1 | 1 | 0 | 0 |
f | 0 | 1 | 1 | 1 | 0 | 0 | 1 | 0 | 0 | 0 |
g | 0 | 0 | 1 | 1 | 0 | 0 | 1 | 1 | 0 | 0 |
h | 1 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 1 |
One mode matrix (students x students) | ||||||||
---|---|---|---|---|---|---|---|---|
ID | a | b | c | d | e | f | g | h |
a | 5 | 1 | 1 | 2 | 2 | 3 | 2 | 3 |
b | 1 | 4 | 3 | 1 | 4 | 2 | 2 | 1 |
c | 1 | 3 | 5 | 1 | 4 | 1 | 2 | 2 |
d | 2 | 1 | 1 | 4 | 1 | 1 | 0 | 4 |
e | 2 | 4 | 4 | 1 | 5 | 2 | 2 | 2 |
f | 3 | 2 | 1 | 1 | 2 | 4 | 3 | 1 |
g | 2 | 2 | 2 | 0 | 2 | 3 | 4 | 0 |
h | 3 | 1 | 2 | 4 | 2 | 1 | 0 | 5 |
One mode matrix (classes x classes) | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|
ID | writer | comTheo | pr | adv | broadc | internet | camShoot | edit | newMedia | CMC |
writer | 4 | 3 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 1 |
comTheo | 3 | 6 | 2 | 2 | 2 | 2 | 3 | 2 | 3 | 2 |
pr | 1 | 2 | 3 | 3 | 0 | 0 | 2 | 1 | 1 | 0 |
adv | 1 | 2 | 3 | 3 | 0 | 0 | 2 | 1 | 1 | 0 |
broadc | 2 | 2 | 0 | 0 | 3 | 1 | 3 | 3 | 0 | 0 |
internet | 2 | 2 | 0 | 0 | 1 | 3 | 1 | 1 | 2 | 2 |
camShoot | 2 | 3 | 2 | 2 | 3 | 1 | 5 | 4 | 0 | 0 |
edit | 2 | 2 | 1 | 1 | 3 | 1 | 4 | 4 | 0 | 0 |
newMedia | 2 | 3 | 1 | 1 | 0 | 2 | 0 | 0 | 3 | 2 |
CMC | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 2 |
Suppose that this is web sites (nodes composed with links)
ID | writer | comTheo | pr | adv | broadc | internet | camShoot | edit | newMedia | CMC |
---|---|---|---|---|---|---|---|---|---|---|
writer | 4 | 3 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 1 |
comTheo | 3 | 6 | 2 | 2 | 2 | 2 | 3 | 2 | 3 | 2 |
pr | 1 | 2 | 3 | 3 | 0 | 0 | 2 | 1 | 1 | 0 |
adv | 1 | 2 | 3 | 3 | 0 | 0 | 2 | 1 | 1 | 0 |
broadc | 2 | 2 | 0 | 0 | 3 | 1 | 3 | 3 | 0 | 0 |
internet | 2 | 2 | 0 | 0 | 1 | 3 | 1 | 1 | 2 | 2 |
camShoot | 2 | 3 | 2 | 2 | 3 | 1 | 5 | 4 | 0 | 0 |
edit | 2 | 2 | 1 | 1 | 3 | 1 | 4 | 4 | 0 | 0 |
newMedia | 2 | 3 | 1 | 1 | 0 | 2 | 0 | 0 | 3 | 2 |
CMC | 1 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | 2 |
위와 같은 이원데이터 (binary matrix 혹은 two-mode matrix data)는 여러 다른 곳에서도 활용될 수 있다는 점이다. 사실, IT의 역사에 남을 Google 또한 이 방법을 이용하여 search engine으로서의 위상을 높혔다. 이 외에도 아래의 예가 있다.
please refer to https://www.r-bloggers.com/concor-in-r/
also note:
#REPLICATE BREIGER ET AL. (1975) #INSTALL CONCOR install.packages("devtools") devtools::install_github("aslez/concoR") #LIBRARIES library(concoR) library(sna) #LOAD DATA data(bank_wiring) bank_wiring #CHECK INITIAL CORRELATIONS (TABLE III) m0 <- cor(do.call(rbind, bank_wiring)) round(m0, 2) #IDENTIFY BLOCKS USING A 4-BLOCK MODEL (TABLE IV) blks <- concor_hca(bank_wiring, p = 2) blks #CHECK FIT USING SNA (TABLE V) #code below fails unless glabels are specified blk_mod <- blockmodel(bank_wiring, blks$block, glabels = names(bank_wiring), plabels = rownames(bank_wiring[[1]])) blk_mod plot(blk_mod)
> #REPLICATE BREIGER ET AL. (1975) > #INSTALL CONCOR > devtools::install_github("aslez/concoR") Skipping install of 'concoR' from a github remote, the SHA1 (618d8be5) has not changed since last install. Use `force = TRUE` to force installation > > #LIBRARIES > library(concoR) > library(sna) > > #LOAD DATA > data(bank_wiring) > bank_wiring $Liking I1 I3 W1 W2 W3 W4 W5 W6 W7 W8 W9 S1 S2 S4 I1 0 0 0 0 1 0 0 0 0 0 0 0 0 0 I3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W1 0 0 0 0 1 1 0 0 0 0 0 1 0 0 W2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W3 1 0 1 0 0 1 0 0 0 0 0 1 0 0 W4 0 0 1 0 1 0 0 0 0 0 0 1 0 0 W5 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W7 0 0 0 0 0 0 0 0 0 1 1 1 0 0 W8 0 0 0 0 0 0 0 0 1 0 1 0 0 1 W9 0 0 0 0 0 0 0 0 1 1 0 0 0 1 S1 0 0 1 0 1 1 0 0 1 0 0 0 0 0 S2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S4 0 0 0 0 0 0 0 0 0 1 1 0 0 0 $Games I1 I3 W1 W2 W3 W4 W5 W6 W7 W8 W9 S1 S2 S4 I1 0 0 1 1 1 1 0 0 0 0 0 0 0 0 I3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W1 1 0 0 1 1 1 1 0 0 0 0 1 0 0 W2 1 0 1 0 1 1 0 0 0 0 0 1 0 0 W3 1 0 1 1 0 1 1 0 0 0 0 1 0 0 W4 1 0 1 1 1 0 1 0 0 0 0 1 0 0 W5 0 0 1 0 1 1 0 0 1 0 0 1 0 0 W6 0 0 0 0 0 0 0 0 1 1 1 0 0 0 W7 0 0 0 0 0 0 1 1 0 1 1 0 0 1 W8 0 0 0 0 0 0 0 1 1 0 1 0 0 1 W9 0 0 0 0 0 0 0 1 1 1 0 0 0 1 S1 0 0 1 1 1 1 1 0 0 0 0 0 0 0 S2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S4 0 0 0 0 0 0 0 0 1 1 1 0 0 0 $Antagonism I1 I3 W1 W2 W3 W4 W5 W6 W7 W8 W9 S1 S2 S4 I1 0 1 0 1 0 0 0 0 0 0 0 0 0 0 I3 1 0 0 0 0 0 1 1 1 1 1 0 0 1 W1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W2 1 0 0 0 0 0 0 0 1 1 1 0 0 0 W3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W4 0 0 0 0 0 0 1 0 0 0 0 0 0 0 W5 0 1 0 0 0 1 0 1 1 1 1 1 1 0 W6 0 1 0 0 0 0 1 0 1 0 0 0 0 0 W7 0 1 0 1 0 0 1 1 0 0 0 0 0 0 W8 0 1 0 1 0 0 1 0 0 0 0 0 0 0 W9 0 1 0 1 0 0 1 0 0 0 0 0 0 0 S1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 S2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 S4 0 1 0 0 0 0 0 0 0 0 0 0 0 0 $Helping I1 I3 W1 W2 W3 W4 W5 W6 W7 W8 W9 S1 S2 S4 I1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 I3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W1 0 0 0 0 1 0 0 0 0 0 1 1 0 0 W2 0 0 0 0 1 1 0 0 0 0 0 1 0 0 W3 0 0 0 1 0 0 0 0 0 0 0 0 0 0 W4 0 0 1 0 1 0 0 1 0 0 0 0 0 0 W5 0 0 0 0 1 0 0 0 0 0 0 0 0 0 W6 0 0 0 0 1 0 0 0 1 1 1 0 0 0 W7 0 0 0 0 0 0 0 0 0 0 0 0 0 1 W8 0 0 0 0 0 0 0 1 1 0 1 0 0 0 W9 0 0 0 0 0 0 0 0 0 0 0 0 0 1 S1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 S2 0 0 0 0 0 0 0 1 0 0 0 0 0 0 S4 0 0 0 0 0 1 0 0 0 1 0 0 0 0 $Windows I1 I3 W1 W2 W3 W4 W5 W6 W7 W8 W9 S1 S2 S4 I1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 I3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W3 0 0 0 0 0 0 0 0 0 0 0 0 0 0 W4 0 0 0 0 0 0 1 1 1 0 1 0 0 0 W5 0 0 0 0 0 1 0 1 0 0 0 1 0 0 W6 0 0 0 0 0 1 1 0 1 1 1 1 0 1 W7 0 0 0 0 0 1 0 1 0 1 1 0 0 1 W8 0 0 0 0 0 0 0 1 1 0 1 1 0 1 W9 0 0 0 0 0 1 0 1 1 1 0 1 0 0 S1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 S2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 S4 0 0 0 0 0 0 0 1 1 1 0 1 0 0 > > #CHECK INITIAL CORRELATIONS (TABLE III) > m0 <- cor(do.call(rbind, bank_wiring)) > round(m0, 2) I1 I3 W1 W2 W3 W4 W5 W6 W7 W8 W9 S1 S2 S4 I1 1.00 -0.11 0.41 0.27 0.17 0.27 0.27 -0.07 0.00 0.03 0.02 0.37 -0.04 -0.03 I3 -0.11 1.00 -0.14 0.41 -0.17 -0.07 0.27 0.05 0.00 -0.08 -0.09 -0.08 0.36 -0.15 W1 0.41 -0.14 1.00 0.30 0.58 0.46 0.07 -0.12 -0.08 -0.23 -0.24 0.34 -0.05 -0.19 W2 0.27 0.41 0.30 1.00 0.18 0.17 0.46 -0.12 -0.26 -0.23 -0.24 0.05 -0.05 -0.19 W3 0.17 -0.17 0.58 0.18 1.00 0.38 -0.04 -0.20 -0.10 -0.21 -0.15 0.35 -0.06 -0.24 W4 0.27 -0.07 0.46 0.17 0.38 1.00 0.03 0.03 0.03 0.09 -0.09 0.56 0.22 -0.07 W5 0.27 0.27 0.07 0.46 -0.04 0.03 1.00 0.11 -0.04 0.01 0.07 0.01 -0.07 0.11 W6 -0.07 0.05 -0.12 -0.12 -0.20 0.03 0.11 1.00 0.33 0.33 0.38 0.09 0.22 0.38 W7 0.00 0.00 -0.08 -0.26 -0.10 0.03 -0.04 0.33 1.00 0.45 0.50 0.08 0.19 0.30 W8 0.03 -0.08 -0.23 -0.23 -0.21 0.09 0.01 0.33 0.45 1.00 0.58 0.07 0.21 0.36 W9 0.02 -0.09 -0.24 -0.24 -0.15 -0.09 0.07 0.38 0.50 0.58 1.00 0.05 0.20 0.43 S1 0.37 -0.08 0.34 0.05 0.35 0.56 0.01 0.09 0.08 0.07 0.05 1.00 0.21 -0.08 S2 -0.04 0.36 -0.05 -0.05 -0.06 0.22 -0.07 0.22 0.19 0.21 0.20 0.21 1.00 -0.05 S4 -0.03 -0.15 -0.19 -0.19 -0.24 -0.07 0.11 0.38 0.30 0.36 0.43 -0.08 -0.05 1.00 > > #IDENTIFY BLOCKS USING A 4-BLOCK MODEL (TABLE IV) > blks <- concor_hca(bank_wiring, p = 2) > blks block vertex 1 1 I1 6 2 I3 2 1 W1 7 2 W2 3 1 W3 4 1 W4 8 2 W5 9 3 W6 11 4 W7 12 4 W8 13 4 W9 5 1 S1 10 3 S2 14 4 S4 > > #CHECK FIT USING SNA (TABLE V) > #code below fails unless glabels are specified > blk_mod <- blockmodel(bank_wiring, blks$block, + glabels = names(bank_wiring), + plabels = rownames(bank_wiring[[1]])) > blk_mod Network Blockmodel: Block membership: I1 I3 W1 W2 W3 W4 W5 W6 W7 W8 W9 S1 S2 S4 1 2 1 2 1 1 2 3 4 4 4 1 3 4 Reduced form blockmodel: Liking Block 1 Block 2 Block 3 Block 4 Block 1 0.70 0 0 0.0500000 Block 2 0.00 0 0 0.0000000 Block 3 0.00 0 0 0.0000000 Block 4 0.05 0 0 0.8333333 Games Block 1 Block 2 Block 3 Block 4 Block 1 0.9 0.60000000 0.000 0.00000000 Block 2 0.6 0.00000000 0.000 0.08333333 Block 3 0.0 0.00000000 0.000 0.37500000 Block 4 0.0 0.08333333 0.375 1.00000000 Antagonism Block 1 Block 2 Block 3 Block 4 Block 1 0.0000000 0.2666667 0.000 0.0000000 Block 2 0.2666667 0.3333333 0.500 0.8333333 Block 3 0.0000000 0.5000000 0.000 0.1250000 Block 4 0.0000000 0.8333333 0.125 0.0000000 Helping Block 1 Block 2 Block 3 Block 4 Block 1 0.2000000 0.06666667 0.100 0.1000000 Block 2 0.2666667 0.00000000 0.000 0.0000000 Block 3 0.1000000 0.00000000 0.500 0.3750000 Block 4 0.0500000 0.00000000 0.125 0.4166667 Windows Block 1 Block 2 Block 3 Block 4 Block 1 0.0000000 0.1333333 0.2000000 0.2500000 Block 2 0.1333333 0.0000000 0.1666667 0.0000000 Block 3 0.2000000 0.1666667 0.0000000 0.5000000 Block 4 0.2500000 0.0000000 0.5000000 0.8333333 > plot(blk_mod) >
Introductions to sna
PADGETT FLORENTINE FAMILIES
DATASET PADGETT and PADGW / Pajek
DESCRIPTION PADGETT
Two 16×16 matrices:
PADGB symmetric binary
PADGM symmetric binary
PADGW
One 16×3 matrix, valued.
BACKGROUND Breiger & Pattison (1986), in their discussion of local role analysis, use a subset of data on the social relations among Renaissance Florentine families (person aggregates) collected by John Padgett from historical documents. The two relations are business ties (PADGB - specifically, recorded financial ties such as loans, credits and joint partnerships) and marriage alliances (PADGM).
As Breiger & Pattison point out, the original data are symmetrically coded. This is acceptable perhaps for marital ties, but is unfortunate for the financial ties (which are almost certainly directed). To remedy this, the financial ties can be recoded as directed relations using some external measure of power - for instance, a measure of wealth. PADGW provides information on (1) each family's net wealth in 1427 (in thousands of lira); (2) the number of priorates (seats on the civic council) held between 1282- 1344; and (3) the total number of business or marriage ties in the total dataset of 116 families (see Breiger & Pattison (1986), p 239).
Substantively, the data include families who were locked in a struggle for political control of the city of Florence in around 1430. Two factions were dominant in this struggle: one revolved around the infamous Medicis (9), the other around the powerful Strozzis (15).
REFERENCES
DL N=16 NM=2 FORMAT = FULLMATRIX DIAGONAL PRESENT ROW LABELS: ACCIAIUOL ALBIZZI BARBADORI BISCHERI CASTELLAN GINORI GUADAGNI LAMBERTES MEDICI PAZZI PERUZZI PUCCI RIDOLFI SALVIATI STROZZI TORNABUON COLUMN LABELS: ACCIAIUOL ALBIZZI BARBADORI BISCHERI CASTELLAN GINORI GUADAGNI LAMBERTES MEDICI PAZZI PERUZZI PUCCI RIDOLFI SALVIATI STROZZI TORNABUON LEVEL LABELS: PADGM PADGB DATA: 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 0 1 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0
DL NR=16 NC=3 FORMAT = FULLMATRIX DIAGONAL PRESENT ROW LABELS: ACCIAIUOL ALBIZZI RIDOLFI STROZZI BARBADORI BISCHERI CASTELLAN GUADAGNI LAMBERTES MEDICI PAZZI PERUZZI SALVIATI TORNABUON GINORI PUCCI COLUMN LABELS: WEALTH #PRIORS #TIES DATA: 10 53 2 36 65 3 27 38 4 146 74 29 55 0 14 44 12 9 20 22 18 8 21 14 42 0 14 103 53 54 48 0 7 49 42 32 10 35 5 48 0 7 32 0 9 3 0 1
QAP result
QAP CORRELATION -------------------------------------------------------------------------------- Data Matrices: PADGB PADGM # of Permutations: 5000 Random seed: 8954 Method: Fast: no missing values allowed QAP results for PADGM * PADGB (5000 permutations) 1 2 3 4 5 6 7 8 Obs Value Significa Average Std Dev Minimum Maximum Prop >= O Prop <= O --------- --------- --------- --------- --------- --------- --------- --------- Pearson Correlation 0.3719 0.0016 0.0032 0.0944 -0.1690 0.4395 0.0016 0.9998 QAP Correlations 1 2 PADGB PADGM ----- ----- 1 PADGB 1.000 0.372 2 PADGM 0.372 1.000 QAP P-Values 1 2 PADGB PADGM ----- ----- 1 PADGB 0.000 0.002 2 PADGM 0.002 0.000 QAP statistics saved as datafile QAP Correlation Results ---------------------------------------- Running time: 00:00:01 Output generated: 12 5 16 08:44:36 UCINET 6.528 Copyright (c) 1992-2012 Analytic Technologies