11 network level indicators bird’s eye view of network image matrix example of network level many...
TRANSCRIPT
11
Network Level Indicators
• Bird’s eye view of network
• Image matrix example of network level
• Many network level measures
• Some would argue this is the most appropriate level of analysis
22
Size
• Number of nodes (people) in the network
• Matters because as size increases– Density decreases– Clustering increases
• Reflects network boundary
• Should always be included as a covariate
33
Density
• Structural property
• Given by
)1(
nn
lD
• Should always be included as covariate as well
44
Density & Size Negatively Correlated
• In STEP study we have data from 24 coalitions at baseline
• We correlated size and density and discovered a negative association as predicted:
• R=-0.69
55
Reciprocity (Mutuality, Symmetry)
• Mutual ties: A B then BA• Some relations are inherently symmetric or
asymmetric– Who did you have lunch with?– Who did you go to for advice?
• Reciprocity is calculated as the percent of ties that are reciprocated:
)1()1(
)1(&)1(
jiij
jiij
AorA
AAR
66
Triads & Transitivity
• Holland & Leinhardt introduced the concept of triads and a triad census
• In a directed graph there are 16 possible triads:– AB BC AC
– AB BC CA
– ….
• One can do a triad census of a network calculating the percent of triads of each type in the network
7
MAN (Mutual, Asymmetric, Null) Census
003 012 102 021D
021U 021C 111D 111U
030T 030C 201 120D
120U 120C 210 300
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Triads & Transitivity (cont.)
• Most often concerned with transitivity• A transitive triad occurs if:
– AB BC – Implies– AC
• Transitivity implies balance, and balance theory is one of the foundations of many behavioral theories
• It is believed that people seek balance both toward others and objects (Heider)
• If a person is imbalanced, this creates cognitive dissonance and people will try to reduce cognitive dissonance (Festinger)
9
Transitive Triad
A B
C
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Transitivity
• The percent of transitive triads provides a measure of cohesion
• In the STEP study we found an average of 17% of triads were transitive.
1111
4 Nodes?
• One might expect the next level of analysis to increase to 4 nodes, as reciprocity was 2 nodes, and triads 3 nodes, but
• 4 nodes takes us to groups (this is where cycles come in)
• And back to the lecture on groups
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Diameter/Ave. Path Length
• Diameter: Length of the longest path in the network
• Ave path length/characteristic path length
• Average of all the distances between nodes
• A measure of network size
13
Average and Maximum Change in Cohesion for each Link Removed
-4-2
02
4pb
dmax
/pbd
el/p
bam
ax/p
badd
0 .2 .4 .6 .8 1density
pbdmax pbdel
pbamax pbadd
14
Cohesion: Measure of how close everyone is, on average, in the network
14
)1(
1
nn
dCohesion
ij
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Unconnected Nodes
• Distances are important to calculate in networks• What about unconnected nodes• Distance equals infinity
– Creates intractable math calculations– Substitute some finite number– Defensible on the grounds that if a node is included in a
network it is reachable because it is in the same set– Might not be reachable because of measurement error– Might not be reachable because of instrumentation
(e.g., 5 closest friends)
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What to substitute for unconnected nodes?
Choices:• N-1
– Advantages: is the maximum theoretical distance between nodes in any network
• N– Advantages: is linearly related to max distance and would be the
distance if a node were deleted
• Max. path length plus 1– Advantages: is intuitively more meaningful
Most Use N-1
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Clustering
• Watts re-introduced the clustering coefficient:
• Average of the individual personal network densities:
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Personal Network Density
PN Density = 1/6 = 16.7% PN Density = 3/6 = 50.0%
A
z
x
y
z
x
yB
1919
Centralization
• The degree ties are focused on one or a few people
• Index ranges from 0 to 1 with 1 being perfectly centralized.
• Recall: Centralized network are ‘scale free’ networks
2020
Examples of Dense Networks (Density=36.4%)
Decentralized (9.1%) Centralized (50.9%)
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Examples of Sparse Networks (Density=18.2%)
Decentralized (0.0%) Centralized (87.3%)
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Centralization Can Be Calculated On All Centrality Measures:
• Centralization Degree:
23
))((2
nn
CCMaxCD DiiD
2323
Centralization (cont.)
• Similar formulas exist for Centralization Closeness, Betweenness, Integration
• Can also be calculated by taking the standard deviation of the centrality scores.
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Core Periphery Structures
• CP Networks have cores of densely connected people and a
• Periphery of those loosely connected to the core and to each other
• Can test whether networks have a C-P structure
25
Core-Periphery Analysis
• A network with a perfect CP structure will have all core nodes connected and peripheral ones connected only to the core
• Construct this idealized matrix and correlate the ideal with the empirical.
• Correlation coefficient is a measure of the CP
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Children’s Health Insurance of Greater LA (CP=0.29)
Provider
CBO
Provider
CBO
Other Provider
Phil
CBOCBO
Policy
Health_Plan
Health_PlanGovt
GovtGovt
Govt
School
Govt
Phil
CBO
Acad
Phil
Policy
Phil
Acad
Phil
Acad
CBO
CBOCBO
School
▲ Missing■ Periphery● Core
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Network Structure & Behavior
• Size clearly matters, large networks:– difficult to coordinate & organize– Norms unclear or diffuse– Diffusion takes longer
• Small networks– Easy to coordinate– Information and behaviors of others are known– Information can travel quickly, but
• Small networks are not powerful
2828
Density
• We discussed earlier the possible curvilinear relationship
• Reciprocity: At the individual level, reciprocated relationship should be more likely associated with behavioral transmission: People more likely influenced by reciprocated relationships;
• On the other hand, advice seeking is asymmetric and one more likely to model those they seek advice from
• Thus, at individual level, reciprocity affects on behavior depend on relationship and behavior
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Data from STEP
3030
Reciprocity & Transitivity
• Networks with high levels of reciprocity:– Diffusion within faster; but – Diffusion between groups slower
• Transitive triads also more likely to:– Increase homogeneity of opinions– Facilitate diffusion within groups, but inhibit
diffusion of outside ideas
3131
Clustering
• High rates of clustering are even more indicative of closed subgroups
• Clustering will inhibit spread between groups but accelerate it within groups
• Higher clustering will increase the importance of bridges that connect clusters
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Centralization
• Centralized networks should/could have fastest diffusion: – Central nodes are key players in the process– Central nodes are gatekeepers– Other properties may interact with
centralization
3333
Core Periphery
• Diffusion more likely to occur in the core
• Take a while for behaviors to filter to the periphery
• Many innovation may come from the periphery then percolate to the core
• Core groups can keep infectious diseases endemic to communities – STDs, HIV, etc.
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2 Mode Data
• Recall that data on events, organizations, etc. can be used to construct 2 mode networks
• E.g., in this class students come from different departments
• Can construct a network based on shared dept. affiliations
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Transposing a Matrix
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Event A Event B Event C
Person 1 1 0 1
Person 2 1 1 0
Person 3 0 1 0
Person N 0 0 1
Matrix A
Person 1 Person 2 Person 3 Person N
Event A 1 1 0 0
Event B 0 1 1 0
Event C 1 0 0 1
Matrix A’ (transpose)
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Excel File
ID SPPD ASC IPR Other
1 1 0 0 0
2 0 0 1 0
3 0 0 1 0
4 1 0 0 0
5 0 0 1 0
6 1 0 0 0
7 0 1 0 0
8 0 1 0 0
9 0 0 1 0
10 0 1 0 0
11 0 0 1 0
12 0 0 0 1
13 1 0 0 0
14 0 1 0 0
15 0 0 0 1
16 1 0 0 0
17 1 0 0 0
18 0 0 1 0
19 0 0 1 0
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Steps
• Read into UCINET as excel file
• Input this file Data\affiliations\dept06
• Creates 1 mode data person by person
• And creates 1 mode dept by dept
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Dept 06 PxP
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
1617
18
19
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Do They Correlate?
• Dept affiliations may lead to who knows whom• We can correlate the 2 matrices• Procedure to do so is know as QAP: Quadratic
Assignment Procedure• This procedures accounts for the dependencies in
the rows and columns• QAP Reg. coefficient between knowing and
department affiliation is 0.30