1. 5th edition of Theory, Methods and Applications of Social Networks.
International Seminar "Personal Networks: Methods and Applications"

2. Motivation
• In some ways, personal or ego network analysis has
been poor cousin of full network analysis
– Concepts that are emblematic of network analysis, such as
betweenness, closeness, and blockmodeling, are full
network analysis concepts
• Yet …
– a great deal of network research is actually based on ego
networks
– Fundamental processes of tie formation and influence
occur at the level of individual choices and behavior
– It can be argued, like politics, that all network processes
are local
NOTE: I use “ego network” and “personal network” interchangeably

3. All is local
• Part of the idea of network
and complexity science is
that faraway events can
affect each other through
causal chains
– Butterfly flapping wings in
China ultimately determines a
hurricane in Florida
• Yet I only interact with
those that I interact with: if
a faraway node affects me,
it is through my interactions
with my alters
– My alters mediate my
interaction with the world
Ann affects Bill, but only because she affects
Pam, who affects Holly who affects those that
interact with Bill

4. This is why I like eigenvector centrality
• Even though it is a global
measure that requires full
network data …
… it is grounded in a principle of
local action
• The centrality of node i is a
simple sum of the
centralities of i’s alters (the
js)
– The centralities of the js are
determined by their ties to
nodes outside i’s circle but
that doesn’t matter because
that information is already
encoded in each j’s score


j
jiji vav 1

vi is the centrality of i, λ is a
constant, aij is the tie from i to j,
vj is the centrality of j
v(a) = .5/1.7 = 0.29
v(b) = (.29+.58)/1.7 = 0.50
v(c) = (.5+.5)/1.7 = 0.58
v(d) = (.29+.58)/1.7 = 0.50
v(e) = .5/1.7 = 0.29
a b c ed
0.29 0.50 .58 0.290.50
λ = 1.73205

5. Clarifying the topic …
• Personal or ego networks can result from:
– Data collected via ego network research design*
– Subgraphs collected via full network design
*Referred to in this workshop as “personal network research”

6. Objective
• Compare the analysis of ego
networks derived from these two
different kinds of research designs
– ENRD: ego network research design*
– FNRD: full network research design
• Given that I’m only interested in
the direct network neighborhood
of a node, can I just use ego
network research design?
– or are there reasons why FNRD is
superior even for investigating local
effects?
*Referred to in this workshop as “personal network research”

7. Ego network research design (ENRD)
• Sample a set of nodes (egos) from a population
• For each ego, obtain list of nodes (alters) the ego
has ties with
– For each alter, ask ego about
• Ego’s relationship with alter:
are they friends? co‐workers? kin?
• Ego’s perceptions of the alter’s
attributes: how old is alter?
How happy is alter?
– Ideally, ask ego to indicate ties among the alters
Mary

8. Results of ego network data collection
• Ego
– attributes
• Alters
– List of alters
– Multiple kinds of ties to ego
– Perceived attributes of alters
– (ties among alters)
Mary

9. Full network research design (FNRD)
• Select a set of actors to be the
nodes
– Typically a culturally defined
group such as a gang, an
organization, a department,
attendees of an event, etc.
• Collect ties (usually of various
kinds/relations) among the
actors
• For comparability with ENRD,
sample egos from full network
and extract the sub‐graph of
ties and nodes incident on the
ego

10. General comparison ENRD and FNRD
Advantage: FNRD
• Can answer context questions
– how fragmented is the network as
a whole?
– How many links separate egos from
each other along the shortest
path?*
• Have incoming ties as well as out
– ENRD can ask ego who likes him,
but is still ego naming the alters
• Non‐ties are meaningful
– Can model tie/non‐tie for each
dyad as outcome of decision‐
making process
– in ENRD can ask ‘who do you not
like?’, but not ‘who do you have no
tie to?’
Advantage: ENRD
• Can employ standard
sampling techniques
– And so standard statistical
methods
• Cheaper & easier to deploy
– Can collect richer data – more
ties
• Fewer privacy/ethical issues
– May improve validity of data
*how quickly can something flowing through the network reach this node?

11. Comparison – cont.
• ENRD combines advantages of mainstream social
science data with social network perspective
• And many network phenomena and measures do not
seem to require anything more than ego network data
– E.g., degree centrality, homophily, structural holes
• Or is this a misconception?
Full
Network
Analysis
Mainstream
Social Science
Ego
Networks
perspectivedata

12. What can you do with ego net data?
• (if you only have) Ties to alters
– Network size for each kind of tie (e.g., number of
friends)
• (if you also have) Alter attributes
– Network composition (e.g., number of friends who
are top‐level managers)
• Testing homophily
• (if you also have) Ties among alters
– Structural holes
– All group‐level network measures (e.g., density of ties
among friends; avg distance; no. of components)

13. Number of ties
• Basically, this is network size
– Can be calculated different size for each type of tie, or all
ties combined
• Very well studied variable; has been very productive
– Health, power, satisfaction
• And there is still more to study
– Number of negative ties is understudied
• how many enemies, rivals, competitors, energy‐drains do you
have?
– Multiplex ties
• Suppose most of your friends are also co‐workers
– Most relationships A—B consist of both the friend and co‐worker tie
• What are the consequences for ego? Less freedom? More strain?

14. Does FNRD have any advantages for
studying network size?
• In context of defined groups, concept of non‐ties is
meaningful
– E.g., can compare ego1’s number of work friends with
ego2’s number of work friends, because we know many
friends were possible in each work setting
– If we want to use no. of friends as measure of ability to
make friends, we want to divide by size of potential
partners pool
1 2

15. Network Composition
• Measures summarizing the kinds of people in a
person’s ego network
– Frequencies (e.g., number of rich friends)
– Central tendency (e.g., avg wealth of friends)
• Measures describing the amount of
heterogeneity in a person’s ego network
– Heterogeneity measures (e.g., Blau, IQV)
• Measures describing the similarity of a person’s
own attributes with those of their alters
– E.g., homophily measures

16. Network Composition
Property of network: Categorical Attributes Continuous Attributes
Summary of kind of alters ego
tends has, based on a given
attribute (e.g., wealth)
Example: Does ego have
mostly rich or mostly poor
friends? How many of each?
Example: What is the average
wealth of ego’s friends?
Measures: frequencies,
proportions
Measures: mean, median
Variability in the kinds of alter
an ego has, based on given
attribute
Example: whether ego has
equal number of rich, middle,
and poor friends, or mostly
one kind
Example: variance in wealth of
person’s friends
Measures: Blau/Herfindahl
heterogeneity; Agresti IQV
Measures: std deviation,
variance
Similarity of ego to alters with
respect to given attribute
Example: Prop. of ego’s friends
who are same wealth class as
ego
Example: Similarity between
ego’s wealth and friends’
wealth
Measures: E‐I index; PBSC;
Yules Q; Q modularity
Measures: avg euclidean
distance; identity coef.

17. Direction of Causality
SELECTION
Ego selects alters based on
attributes
INFLUENCE
Ego influences alters to
have attribute
Summary of kind of alter
an ego tends to have,
based on a given attribute
(e.g., wealth)
Ego tends to seek out rich
friends
Ego tends to give friends
money, opportunities for
investment
Variability in the kinds of
alter an ego has, based on
given attribute
Ego seeks out diversity and
has the skills to manage it
Ego encourages alters to
develop unique
perspectives
Similarity of ego to alters
with respect to given
attribute
Homophily: ego attracted
by people similar to self
Diffusion: ego’s attitudes
are contagious

18. Measurement of homophily*
• What we can measure is the extent to which
alters resemble egos
• So, can we measure homophily using ENRD
data?
– It would seem obvious that we can …
*Actually, measurement of similarity – homophily implies a certain direction of
causality which can only be inferred by other means, if at all

19. Who do you discuss important
matters with?
Male Female
Male 1245 748
Female 970 1515
Age < 30 30-39 40-49 50-59 60+
< 30 567 186 183 155 56
30 - 39 191 501 171 128 106
40 - 49 88 170 246 84 70
50 - 59 84 100 121 210 108
60 + 34 127 138 212 387
White Black Hisp Other
White 3806 29 30 20
Black 40 283 4 3
Hisp 66 6 120 1
Other 21 5 3 34
Source:
Marsden, P.V. 1988. Homogeneity in confiding
relations. Social Networks 10: 57‐76.
General Social Survey 1985. Ego network study of 1500 Americans
• Rows are egos
• Columns are alters
• Cells are no. of ties
from type of ego to
type of alter

20. Homophily at the individual level
• Across all alters for a given ego, compute
simple frequencies for the variable “has same
attribute value as ego or not”
– E.g., number of alters that are same gender as ego
and number of alters that are not
• Define a statistic such as percent homophily
Same attrib
value
1 0
Tie = 1 a b
%H = 9/54 = 0.83
Same attrib
value as ego
1 0
Tie = 1 45 9
%H = a/(a+b)

21. Alternative statistic: E‐I index
• Given frequencies, compute
– It is just a rescaling of %H = a/(a+b)
• Example:
Same attrib
value
1 0
Tie = 1 a b
Same gender
as ego
1 0
Tie = 1 45 9
ab
ab
EI



EI = (9‐45)/(9+45) = ‐0.667

22. But there is a small problem
• Suppose we did know the full network. As a
result, for a given ego we know their non‐ties as
well
• Both %H and E‐I show strong homophily
– Yet probability of being same gender is same for ties
and non‐ties
– IOW, no preference for same gender. Independence.
• The result from ENRD data is misleading
Same attrib
value
1 0
Has
tie
1 45 9
0 45 9
%H = 0.83
EI = ‐0.667

23. With FNRD we can define better
measures of homophily/influence
• Yule’s Q takes into account non‐choices as
well:
• Example cases
Same attrib
value
1 0
Tie
1 a b
0 c d
%H = 0.83, EI = ‐0.667, YQ = 0.00
bcad
bcad
YQ



Same attrib
value
1 0
Tie
1 45 9
0 45 9
Same attrib
value
1 0
Tie
1 45 9
0 9 45
%H = 0.83, EI = ‐0.667, YQ = 0.92

24. Invariance property of Yule’s Q
• Additional benefit of
Yule’s Q is insensitivity to
table marginals
– What if you dichotomized
at different level and now
had twice as many 1s?
• keeping preference for
own kind the same
– What if you had twice as
many same‐gender pairs
• but with same underlying
preference for own kind?
Same
attrib
1 0
Tie
1 45 9
0 101 151
YQ = 0.76
%H = 0.83
EI = ‐0.67
Same
attrib
1 0
Tie
1 180 18
0 202 151
YQ = 0.76
%H = 0.91
EI = ‐0.82
Twice as many ties and twice
as many same gender dyads
Yule’s Q is not “fooled” by
multiplying a row or column by a
constant
• Takes into account category
sizes
An aside …

25. Homophily: preference vs opportunity
• With ENRDs we have information on ties but not non‐ties
– We can measure homophily as outcome, but not homophily as
choice
• Adequacy of homophily in ENRD depends on research
question
– If am American and 95% of my friends are American, this clearly
has certain effects on me
• even if this is only because 95% of people in my world are American
• So ENRD is ok
– But if I am trying to measure nationalistic tendencies, I need to
know whether 95% is more or less than expected if a person
were making choices without regard for nationality
• If 95% of my non‐ties are also American, we know that I am not
showing any preference for Americans – low nationalism score

26. Comparing individuals
• With ENRD, can we at least
compare egos to each other?
– Some ego’s have higher E‐I
index than others. Is this
interpretable as preference?
• In principle, yes
– if egos are drawn from the
same population, then …
– … significantly higher
homophily score indicates
greater preference for own
kind
• In practice, not clear what
“same population” means
– People live in segregated
worlds due to choices made by
others
• Example: Are male or female
students here at UAB more
homophilous with respect to
ethnic background?
• For each person, we measure
homophily using %H or E‐I
– Run t‐test/anova to compare
genders
• If all students face same ethnic
environment, then significant
difference in avg homophily is
meaningful as difference in
preference

27. Propinquity
• Do people tend to have ties with people who
are physically close by?
0
0.1
0.2
0.3
0.4
0 20 40 60 80 100
Distance (meters)
ProbofDailyCommunication
From research by Tom Allen
Distance
short long
Tie
1 10 500
0 10 500
• Same issues as
homophily
– Lack of non‐ties
a problem for
modeling choice

28. Theoretical criteria
• Until now we have examined specific
phenomena/measurements we are interested
in
– E.g., homophily
• Another way to compare ENRD and FNRD is in
terms of the explanatory mechanisms that are
used to understand node outcomes

29. Perspectives of action in SNA
Structuralist
In the social production of their existence,
men inevitably enter into definite relations,
which are independent of their will, namely
relations of production appropriate to a given
stage in the development of their material
forces of production. The totality of
these relations of production constitutes the
economic structure of society, the real
foundation, on which arises a legal and
political superstructure and to which
correspond definite forms of social conscious‐
ness. The mode of production of material life
conditions the general process of social,
political and intellectual life. It is not the
consciousness of men that determines their
existence, but their social existence that
determines their consciousness.
– Marx 1859 Preface to A Contribution to the
Critique of Political Economy
Cognitivist
“If men define situations as real, they are
real in their consequences”
– W.I. Thomas
Success
information
Actual no. of ties
confidence
Perceived no. of ties

30. Information benefits
of structural holes
• Burt argues that ego2
has an information
advantage over ego1
• It is shape of actual
network of
information flow, not
ego’s perception that
matters
Ego 1
Ego 2

31. Feelings of support & belonging
• Actual shape of network may be secondary to
ego’s perception
Ego 1 Ego 2

32. Power
• Ability to get things done may depend on the
relationship between actual and perceived
networks (Krackhardt) – i.e., accuracy

33. Perception and ENRD
• With ENRD, all ties are perceived by ego
• Therefore, ENRD works well when …
• Predicting ego’s own behavior
• Predicting ego outcomes based on ego’s behavior
• Predicting ego outcomes AND we can assume ego is
accurate in perceiving ties
• Hard to use ENRD when the topic of interest is
understanding perceptual accuracy
– Can use hybrid designs where the alters are
interviewed about ties with ego

34. VARIANT EGO NETWORK DESIGNS

35. Variations in ego net research designs
• Limited 2‐wave snowball
• Key informant method
– Focal individual method

36. 2‐wave snowball
• Get alter list from ego
• Now interview alters about egos and the other
alters
• Allows us to examine accuracy/differences in
perceptions of ties

37. Key informant method
• We are interested in ties among a set of
people, but can’t interview them
– E.g., politicians, celebrities, criminals
• Key informants are asked to provide the entire
network from their point of view
• One version of this is the observational focal
individual method
– Follow key informants around all day and record
interactions around them

38. Rushmore Chimpanzee study
• Julie Rushmore
– College of Veterinary Medicine;
University of Georgia
• Each of 37 chimps is chosen to be
“focal” chimp for a day
• Researcher follows focal chimp for
entire day and records not only
his/her interactions but also all
other interactions within view
• Result is 37 separate 37‐by‐37
matrices
– a 3‐way, 1‐mode data cube
rushmore@uga.edu

39. Sample Data
AJ AL AT AZ BB BL BO BU ES
AJ 33 33 33 33 17 17 20 33
AL 33 42 42 32 11 11 14 32
AT 33 42 42 32 11 11 14 32
AZ 33 42 42 32 11 11 14 32
BB 33 32 32 32 11 11 14 33
BL 17 11 11 11 11 17 17 11
BO 17 11 11 11 11 17 17 11
BU 20 14 14 14 14 17 17 14
ES 33 32 32 32 33 11 11 14
AJ AL AT AZ BB BL BO BU ES
AJ 24 26 24 22 0 0 0 42
AL 24 24 24 19 0 0 0 24
AT 26 24 24 20 0 0 0 25
AZ 24 24 24 19 0 0 0 24
BB 22 19 20 19 0 0 0 20
BL 0 0 0 0 0 0 0 0
BO 0 0 0 0 0 0 0 0
BU 0 0 0 0 0 0 0 0
ES 42 24 25 24 20 0 0 0
Interactions observed while
following AJ
Interactions observed while
following KK
Only first 9 chimps shown from a 37 by 37 matrix

40. Correlations among focal matrices
AJ AL AT AZ BB BL BO BU ES EU KK LK LR ML MS MU MX NP OG OM OT OU PB PG QT RD ST TG TJ TS TT TU UM UN WA WL YB
AJ 1.00 0.66 0.39 0.15 0.58 ‐0.11 0.36 0.25 0.63 0.18 0.68 0.63 0.53 0.58 ‐0.08 0.22 0.26 0.00 0.34 0.24 0.52 0.27 0.34 0.52 0.18 0.15 0.37 0.21 0.17 0.51 0.31 0.00 0.05 0.14 0.06 0.36 0.67
AL 0.66 1.00 0.62 0.55 0.67 0.06 0.45 0.30 0.61 0.34 0.57 0.70 0.56 0.65 ‐0.13 0.23 0.27 0.00 0.48 0.29 0.59 0.24 0.37 0.51 0.38 0.28 0.35 0.34 0.30 0.57 0.29 ‐0.09 0.06 0.01 0.20 0.33 0.76
AT 0.39 0.62 1.00 0.64 0.47 0.26 0.33 0.54 0.44 0.29 0.51 0.63 0.36 0.45 ‐0.12 0.13 0.32 0.17 0.42 0.40 0.56 0.41 0.28 0.32 0.59 0.25 0.27 0.57 0.55 0.54 0.41 ‐0.03 0.27 0.16 0.26 0.48 0.54
AZ 0.15 0.55 0.64 1.00 0.33 0.36 0.31 0.55 0.31 0.32 0.34 0.42 0.26 0.36 ‐0.10 0.05 0.22 0.22 0.47 0.56 0.52 0.45 0.12 0.28 0.57 0.23 0.42 0.68 0.63 0.51 0.45 ‐0.03 0.22 0.05 0.28 0.31 0.31
BB 0.58 0.67 0.47 0.33 1.00 0.35 0.68 0.49 0.74 0.26 0.65 0.64 0.57 0.67 ‐0.14 0.12 0.47 0.09 0.43 0.32 0.59 0.31 0.51 0.57 0.40 0.29 0.32 0.34 0.42 0.56 0.27 ‐0.09 0.15 0.10 0.18 0.42 0.73
BL ‐0.11 0.06 0.26 0.36 0.35 1.00 0.38 0.56 0.16 ‐0.03 0.16 0.14 0.12 0.22 ‐0.17 ‐0.08 0.40 0.08 0.20 0.25 0.24 0.23 0.34 0.06 0.53 0.10 0.06 0.33 0.43 0.13 0.10 ‐0.11 0.13 ‐0.08 0.20 0.30 0.09
BO 0.36 0.45 0.33 0.31 0.68 0.38 1.00 0.63 0.74 0.19 0.59 0.64 0.58 0.62 ‐0.17 ‐0.14 0.37 0.24 0.39 0.28 0.45 0.35 0.25 0.45 0.48 0.13 0.29 0.49 0.47 0.60 0.38 ‐0.14 ‐0.02 ‐0.16 ‐0.10 0.47 0.53
BU 0.25 0.30 0.54 0.55 0.49 0.56 0.63 1.00 0.49 0.09 0.59 0.57 0.32 0.49 ‐0.20 ‐0.03 0.44 0.28 0.51 0.57 0.58 0.65 0.22 0.33 0.75 0.23 0.41 0.80 0.74 0.54 0.56 ‐0.13 0.19 ‐0.10 0.10 0.73 0.40
ES 0.63 0.61 0.44 0.31 0.74 0.16 0.74 0.49 1.00 0.34 0.82 0.75 0.61 0.78 ‐0.13 0.04 0.35 0.20 0.48 0.39 0.62 0.43 0.33 0.74 0.37 0.16 0.35 0.47 0.41 0.72 0.49 ‐0.13 0.11 0.01 0.08 0.54 0.77
EU 0.18 0.34 0.29 0.32 0.26 ‐0.03 0.19 0.09 0.34 1.00 0.21 0.30 0.13 0.27 0.09 0.10 0.11 0.40 0.22 0.22 0.15 0.10 0.04 0.38 0.07 0.00 0.24 0.22 0.19 0.33 0.19 0.09 0.14 0.30 0.20 0.04 0.30
KK 0.68 0.57 0.51 0.34 0.65 0.16 0.59 0.59 0.82 0.21 1.00 0.78 0.48 0.73 ‐0.16 0.11 0.41 0.23 0.54 0.50 0.67 0.62 0.26 0.70 0.48 0.23 0.40 0.57 0.45 0.69 0.62 ‐0.12 0.10 0.03 0.11 0.68 0.70
LK 0.63 0.70 0.63 0.42 0.64 0.14 0.64 0.57 0.75 0.30 0.78 1.00 0.49 0.72 ‐0.20 0.10 0.46 0.20 0.49 0.42 0.66 0.47 0.25 0.59 0.53 0.20 0.43 0.60 0.45 0.71 0.55 ‐0.15 0.06 ‐0.04 0.07 0.66 0.76
LR 0.53 0.56 0.36 0.26 0.57 0.12 0.58 0.32 0.61 0.13 0.48 0.49 1.00 0.46 ‐0.12 ‐0.02 0.11 0.00 0.47 0.24 0.63 0.27 0.46 0.58 0.40 0.10 0.21 0.27 0.29 0.46 0.20 ‐0.10 ‐0.05 ‐0.09 ‐0.04 0.35 0.55
ML 0.58 0.65 0.45 0.36 0.67 0.22 0.62 0.49 0.78 0.27 0.73 0.72 0.46 1.00 ‐0.13 0.31 0.65 0.21 0.46 0.40 0.57 0.42 0.28 0.58 0.40 0.25 0.28 0.47 0.42 0.66 0.46 ‐0.14 0.16 0.14 0.43 0.54 0.76
MS ‐0.08 ‐0.13 ‐0.12 ‐0.10 ‐0.14 ‐0.17 ‐0.17 ‐0.20 ‐0.13 0.09 ‐0.16 ‐0.20 ‐0.12 ‐0.13 1.00 0.02 ‐0.17 0.18 ‐0.13 ‐0.10 ‐0.13 ‐0.11 ‐0.09 ‐0.10 ‐0.21 0.02 ‐0.04 ‐0.14 ‐0.12 ‐0.07 ‐0.07 0.76 0.58 0.44 0.04 ‐0.15 ‐0.10
MU 0.22 0.23 0.13 0.05 0.12 ‐0.08 ‐0.14 ‐0.03 0.04 0.10 0.11 0.10 ‐0.02 0.31 0.02 1.00 0.39 0.00 ‐0.01 ‐0.01 0.00 0.03 0.03 0.00 0.05 0.20 ‐0.08 0.07 ‐0.07 0.14 0.05 ‐0.04 0.13 0.31 0.58 0.09 0.09
MX 0.26 0.27 0.32 0.22 0.47 0.40 0.37 0.44 0.35 0.11 0.41 0.46 0.11 0.65 ‐0.17 0.39 1.00 0.23 0.20 0.23 0.27 0.25 0.23 0.17 0.36 0.06 0.07 0.31 0.36 0.31 0.23 ‐0.11 0.16 0.24 0.47 0.42 0.36
NP 0.00 0.00 0.17 0.22 0.09 0.08 0.24 0.28 0.20 0.40 0.23 0.20 0.00 0.21 0.18 0.00 0.23 1.00 0.34 0.50 0.23 0.48 0.00 0.18 0.21 ‐0.01 0.23 0.46 0.36 0.30 0.55 0.17 0.25 0.31 0.13 0.33 0.07
OG 0.34 0.48 0.42 0.47 0.43 0.20 0.39 0.51 0.48 0.22 0.54 0.49 0.47 0.46 ‐0.13 ‐0.01 0.20 0.34 1.00 0.77 0.71 0.72 0.37 0.50 0.55 0.16 0.43 0.55 0.52 0.41 0.49 ‐0.09 0.13 ‐0.03 0.18 0.53 0.48
OM 0.24 0.29 0.40 0.56 0.32 0.25 0.28 0.57 0.39 0.22 0.50 0.42 0.24 0.40 ‐0.10 ‐0.01 0.23 0.50 0.77 1.00 0.72 0.86 0.26 0.44 0.52 0.15 0.47 0.65 0.58 0.42 0.64 ‐0.06 0.21 0.03 0.24 0.53 0.36
OT 0.52 0.59 0.56 0.52 0.59 0.24 0.45 0.58 0.62 0.15 0.67 0.66 0.63 0.57 ‐0.13 0.00 0.27 0.23 0.71 0.72 1.00 0.70 0.49 0.62 0.61 0.21 0.44 0.58 0.57 0.56 0.52 ‐0.11 0.13 ‐0.01 0.19 0.59 0.69
OU 0.27 0.24 0.41 0.45 0.31 0.23 0.35 0.65 0.43 0.10 0.62 0.47 0.27 0.42 ‐0.11 0.03 0.25 0.48 0.72 0.86 0.70 1.00 0.21 0.40 0.58 0.22 0.37 0.71 0.59 0.51 0.70 ‐0.07 0.20 0.00 0.15 0.70 0.33
PB 0.34 0.37 0.28 0.12 0.51 0.34 0.25 0.22 0.33 0.04 0.26 0.25 0.46 0.28 ‐0.09 0.03 0.23 0.00 0.37 0.26 0.49 0.21 1.00 0.27 0.32 0.06 0.09 0.04 0.12 0.12 ‐0.02 ‐0.08 0.07 0.04 0.14 0.28 0.40
PG 0.52 0.51 0.32 0.28 0.57 0.06 0.45 0.33 0.74 0.38 0.70 0.59 0.58 0.58 ‐0.10 0.00 0.17 0.18 0.50 0.44 0.62 0.40 0.27 1.00 0.30 0.14 0.35 0.39 0.33 0.52 0.41 ‐0.10 0.00 0.00 0.05 0.43 0.69
QT 0.18 0.38 0.59 0.57 0.40 0.53 0.48 0.75 0.37 0.07 0.48 0.53 0.40 0.40 ‐0.21 0.05 0.36 0.21 0.55 0.52 0.61 0.58 0.32 0.30 1.00 0.25 0.20 0.71 0.59 0.47 0.43 ‐0.15 0.09 ‐0.15 0.16 0.64 0.35
RD 0.15 0.28 0.25 0.23 0.29 0.10 0.13 0.23 0.16 0.00 0.23 0.20 0.10 0.25 0.02 0.20 0.06 ‐0.01 0.16 0.15 0.21 0.22 0.06 0.14 0.25 1.00 ‐0.02 0.27 0.30 0.34 0.20 0.00 0.12 ‐0.03 0.15 0.27 0.28
ST 0.37 0.35 0.27 0.42 0.32 0.06 0.29 0.41 0.35 0.24 0.40 0.43 0.21 0.28 ‐0.04 ‐0.08 0.07 0.23 0.43 0.47 0.44 0.37 0.09 0.35 0.20 ‐0.02 1.00 0.40 0.34 0.31 0.39 0.13 0.18 0.07 0.07 0.24 0.34
TG 0.21 0.34 0.57 0.68 0.34 0.33 0.49 0.80 0.47 0.22 0.57 0.60 0.27 0.47 ‐0.14 0.07 0.31 0.46 0.55 0.65 0.58 0.71 0.04 0.39 0.71 0.27 0.40 1.00 0.69 0.72 0.80 ‐0.09 0.21 ‐0.03 0.15 0.67 0.36
TJ 0.17 0.30 0.55 0.63 0.42 0.43 0.47 0.74 0.41 0.19 0.45 0.45 0.29 0.42 ‐0.12 ‐0.07 0.36 0.36 0.52 0.58 0.57 0.59 0.12 0.33 0.59 0.30 0.34 0.69 1.00 0.60 0.56 ‐0.08 0.23 0.03 0.21 0.52 0.37
TS 0.51 0.57 0.54 0.51 0.56 0.13 0.60 0.54 0.72 0.33 0.69 0.71 0.46 0.66 ‐0.07 0.14 0.31 0.30 0.41 0.42 0.56 0.51 0.12 0.52 0.47 0.34 0.31 0.72 0.60 1.00 0.72 ‐0.09 0.15 0.03 0.10 0.52 0.59
TT 0.31 0.29 0.41 0.45 0.27 0.10 0.38 0.56 0.49 0.19 0.62 0.55 0.20 0.46 ‐0.07 0.05 0.23 0.55 0.49 0.64 0.52 0.70 ‐0.02 0.41 0.43 0.20 0.39 0.80 0.56 0.72 1.00 ‐0.06 0.21 0.01 0.10 0.56 0.36
TU 0.00 ‐0.09 ‐0.03 ‐0.03 ‐0.09 ‐0.11 ‐0.14 ‐0.13 ‐0.13 0.09 ‐0.12 ‐0.15 ‐0.10 ‐0.14 0.76 ‐0.04 ‐0.11 0.17 ‐0.09 ‐0.06 ‐0.11 ‐0.07 ‐0.08 ‐0.10 ‐0.15 0.00 0.13 ‐0.09 ‐0.08 ‐0.09 ‐0.06 1.00 0.59 0.49 0.01 ‐0.14 ‐0.10
UM 0.05 0.06 0.27 0.22 0.15 0.13 ‐0.02 0.19 0.11 0.14 0.10 0.06 ‐0.05 0.16 0.58 0.13 0.16 0.25 0.13 0.21 0.13 0.20 0.07 0.00 0.09 0.12 0.18 0.21 0.23 0.15 0.21 0.59 1.00 0.62 0.38 0.10 0.11
UN 0.14 0.01 0.16 0.05 0.10 ‐0.08 ‐0.16 ‐0.10 0.01 0.30 0.03 ‐0.04 ‐0.09 0.14 0.44 0.31 0.24 0.31 ‐0.03 0.03 ‐0.01 0.00 0.04 0.00 ‐0.15 ‐0.03 0.07 ‐0.03 0.03 0.03 0.01 0.49 0.62 1.00 0.45 ‐0.08 0.05
WA 0.06 0.20 0.26 0.28 0.18 0.20 ‐0.10 0.10 0.08 0.20 0.11 0.07 ‐0.04 0.43 0.04 0.58 0.47 0.13 0.18 0.24 0.19 0.15 0.14 0.05 0.16 0.15 0.07 0.15 0.21 0.10 0.10 0.01 0.38 0.45 1.00 0.08 0.20
WL 0.36 0.33 0.48 0.31 0.42 0.30 0.47 0.73 0.54 0.04 0.68 0.66 0.35 0.54 ‐0.15 0.09 0.42 0.33 0.53 0.53 0.59 0.70 0.28 0.43 0.64 0.27 0.24 0.67 0.52 0.52 0.56 ‐0.14 0.10 ‐0.08 0.08 1.00 0.46
YB 0.67 0.76 0.54 0.31 0.73 0.09 0.53 0.40 0.77 0.30 0.70 0.76 0.55 0.76 ‐0.10 0.09 0.36 0.07 0.48 0.36 0.69 0.33 0.40 0.69 0.35 0.28 0.34 0.36 0.37 0.59 0.36 ‐0.10 0.11 0.05 0.20 0.46 1.00
High correlation between AJ and AL indicates that the pattern of interactions among all
chimps when AJ is present is very similar to the pattern when AL is present
AJ AL AT AZ
AJ 1.00 0.66 0.39 0.15
AL 0.66 1.00 0.62 0.55
AT 0.39 0.62 1.00 0.64
AZ 0.15 0.55 0.64 1.00

41. MDS of correlations
AJ
AL
AT
AZ
BB
BL
BO
BU
ES
EU
KKLK
LR
ML
MS
MU
MX
NP
OG
OM
OT
OU
PB
PG
QT
RD
ST
TG
TJ
TS
TT
TU
UM
UN
WA
WL
YB
• Nodes near each other provide similar “views” of the network structure
• Nodes in the core have similar views of the network
– would be good choices as “key informants”
• Nodes in periphery, like MU, would see a distorted view of the network

42. Research Agenda
• Interesting research question is
the relationship between
network position and
perception of the network
• Previous work has centered
on centrality  greater accuracy
• But aside from degree of
accuracy is: what exactly is the
perception from a given
position?
– Systematically different
perceptions by Bill versus Holly
– “point of view” research

43. LONGITUDINAL ANALYSIS

44. Friends named, by week
Newcomb T. (1961). The acquaintance process. New York: Holt& Winston.Copyright (c) 2011 Steve Borgatti & David
Dekker. Do not distribute.
T0 T1 T2 T3 T4 T5 T6 T7 T8 T10 T11 T12 T13 T14 T15
P1 0 1 4 1 7 5 6 6 6 7 2 7 3 7 9
P2 1 1 1 4 9 10 9 9 8 9 9 12 10 10 12
P3 6 1 3 4 8 6 7 6 5 7 8 8 3 4 7
P4 2 2 3 3 7 8 9 9 7 8 7 9 6 8 10
P5 6 1 4 3 6 5 8 8 9 8 8 9 8 9 10
P6 0 3 3 2 5 4 3 5 5 4 3 7 6 6 9
P7 2 3 3 3 7 7 8 7 6 3 3 4 5 6 5
P8 0 0 3 0 5 3 3 4 4 6 6 6 3 7 8
P9 0 1 4 4 9 9 9 9 8 6 9 9 7 8 10
P10 0 0 0 0 0 0 2 1 1 2 1 7 4 4 2
P11 3 4 2 6 6 5 7 4 7 7 5 8 7 8 7
P12 3 4 2 3 3 4 3 4 5 6 7 4 5 6 9
P13 0 1 4 4 8 6 9 6 7 7 5 6 7 7 8
P14 1 4 3 3 1 6 9 8 4 2 9 6 7 2 8
P15 5 3 0 0 0 2 5 0 0 3 2 0 0 1 1
P16 2 2 3 4 4 6 4 5 3 4 4 5 6 6 7
P17 1 5 2 2 7 6 5 5 7 5 8 7 7 7 10

45. No. of friends over time
0
2
4
6
8
10
12
14
T0 T1 T2 T3 T4 T5 T6 T7 T8 T10 T11 T12 T13 T14 T15
P1
P2
P3
P4
P5
P6
P7
P8
P9
P10
P11
P12
P13
P14
P15
P16
P17
Weeks
No. of friends named

46. Individual degree trajectories
y = 0.7321x + 1.7429
R² = 0.7161
0
2
4
6
8
10
12
14
T0 T2 T4 T6 T8 T11 T13 T15
P2
P2
Linear (P2)
y = ‐0.1321x + 2.5238
R² = 0.1069
0
1
2
3
4
5
6
T0 T2 T4 T6 T8 T11 T13 T15
P15
P15
Linear (P15)
y = 0.3071x + 3.2762
R² = 0.5628
0
1
2
3
4
5
6
7
8
9
T0 T2 T4 T6 T8 T11 T13 T15
P11
P11
Linear (P11)
y = 0.3821x + 1.6762
R² = 0.3897
0
2
4
6
8
10
T0 T2 T4 T6 T8 T11 T13 T15
P1
P1
Linear (P1)

47. Slopes and intercepts
• Intercept is general
tendency to name others as
friends
– Gregariousness
• Slope is increase in friends
over time
• Can model via HLM
– Time is L1 unit
– Person is L2 unit
• L2 regression models slope
& intercept as function of
ego characteristics
– Optimism
– Social ability
person intercept slope
1 1.676 0.382
2 1.743 0.732
3 4.362 0.146
4 2.848 0.461
5 3.000 0.475
6 1.133 0.400
7 3.914 0.111
8 0.095 0.471
9 2.800 0.500
10 ‐1.029 0.329
11 3.276 0.307
12 1.933 0.325
13 2.638 0.379
14 2.581 0.286
15 2.524 ‐0.132
16 2.248 0.261
17 2.086 0.439
high increase
low increase
decline

48. Beyond network size
• One ego might show no change in no. of
friends and indeed not gained or lost any ties
• Another ego might show no change as well,
but have lost all initial ties and replaced them
with equal number of completely new alters
• Need to do an analysis at the tie/alter level
Copyright (c) 2011 Steve Borgatti & David
Dekker. Do not distribute.

49. T1Size T1 ties 3
T2Size T2 ties 3
NewTies Ties added at T2 2
LostTies Ties lost 2
KeptTies Ties present both time periods 1
AbsentTies Ties ABSENT both time periods 12
Changes for node RUSS
Changes within ego networks
T1
T2
How many ties that each node
add/drop between time points?

50. Egonet changes
T1
Size
T2
Size
New
Ties
Lost
Ties
Kept
Ties
Abse
nt
Ties
HOLLY 3 3 2 2 1 12
BRAZEY 3 3 2 2 1 12
CAROL 3 3 1 1 2 13
PAM 3 3 1 1 2 13
PAT 3 3 2 2 1 12
JENNIE 3 3 0 0 3 14
PAULINE 3 3 1 1 2 13
ANN 3 3 1 1 2 13
MICHAEL 3 3 0 0 3 14
BILL 3 3 1 1 2 13
LEE 3 3 1 1 2 13
DON 3 3 0 0 3 14
JOHN 3 3 1 1 2 13
HARRY 3 3 1 1 2 13
GERY 3 3 1 1 2 13
STEVE 3 3 0 0 3 14
BERT 3 3 1 1 2 13
RUSS 3 3 2 2 1 12
Women Men
------ ------
1 Mean 1.750 2.200
2 Std Dev 0.661 0.600
3 Sum 14.000 22.000
4 Variance 0.438 0.360
5 SSQ 28.000 52.000
6 MCSSQ 3.500 3.600
7 Euc Norm 5.292 7.211
8 Minimum 1.000 1.000
9 Maximum 3.000 3.000
10 N of Obs 8.000 10.000
Difference Sig
========== =====
-0.450 0.157
Number of ties KEPT
Significance for t‐test obtained via
randomization method
WomenMen

51. Modeling homophily dynamics
• Suppose blue nodes have tendency to …
– Add blue friends over time
– Drop red friends over time
T1 T2
For clarity of exposition, the
pictures are full networks,
but the point is egonet
change
Blue egos show tendency to drop red alters
T1 T2

52. Modeling change as a function of
group membership
‐1 0 1
0 7 151 2
1 14 112 20
Chi‐Square 22.25 p = 0.001
Pearson Corr 0.10 P = 0.029
‐1 0 1 Odds Odds Ratio
0 0.044 0.944 0.013 0.013
12.540
1 0.096 0.767 0.137 0.159
Whether
alter is same
group as ego
Relationship improved (1),
worsened (‐1) or stayed same
P‐value constructed via QAP permutation test

53. The NEW ties
Num
New Number of New ties 2
Num
FoF
Number of ties between
New nodes and T1 alters. 2
Den
FoF
NumFoF divided by max
possible (NumFoF expressed
as a density).
0.3
3
FoF/
T1
NumFoF divided by number
of T1 alters
0.6
7
FoF/
New
NumFoF divided by number
NumNew 1
RED are T1
BLUE are T2
GRAY are in both
Making friends with friends’ friends

54. E0A0 = Null triads (no ties)
E0A1 = Ego has not ties but the two potential alters are tied.
E1A0 = Ego has tie to one alter; other potential alter is isolate.
E1A1 = Ego has tie to one alter, who is tied to the other potential alter.
E2A0 = (Brokerage) Ego has ties to both alters, who are not tied to each other.
E2A1 = (No brokerage) Ego has ties to both alters, who are tied to each other.
T1
T2
E0A0 E0A1 E1A0 E1A1 E2A0 E2A1
E0A0 56 2 20 2 0 0 80
E0A1 6 14 0 4 0 1 25
E1A0 15 4 14 2 0 0 35
E1A1 3 4 0 1 1 1 10
E2A0 0 0 2 0 0 0 2
E2A1 1 0 0 0 0 0 1
81 24 36 9 1 2 153
Changes in ego’s triads
RED are T1
BLUE are T2
GRAY are in both

55. CONCLUDING REMARKS

56. Summary
• Distinguished between ego networks and ego
network research design (personal network
analysis)
• Asked whether there are any
advantages/disadvantages to ENRD vs FNRD
when only interested in ego network variables

57. Summary effects vs underlying
tendencies
• Measurements of network size, homophily,
propinquity etc can be used in two ways
– Summary of ego’s exposure to what flows
• Function of opportunities provided by environment
– Indication of ego’s strategies in tie formation
• Choices being made by the ego
• Examples
– Network size vs ability to make friends
– Observed exogamy vs preference for out marriage
ENRD FNRD
Overall effects Underlying tendencies
Consequences of homophily Reveal cognitive characteristics

58. Structuralist vs cognitivist mechanisms
• Some theoretical
mechanisms imply that
perceptions of the
network don’t matter
– Information benefits of
central position
• Others depend crucially
on perceptions
– My behavior is based on
my perceptions
• Outcomes vs behavior
• In pure ENRDs, all ties
are perceived
– Lack of true incoming
ties
– Very strong for
understanding behavior
– For understanding
outcomes, we need
additional assumption of
accuracy of perception
– People vary in
perception accuracy

59. More generally
• FRNDs useful for studying global network phenomena (of course)
• But fundamental processes of tie formation and influence occur at
the level of individual choices and behavior
– Personal network analysis at the center of the network dynamics field
(or should be)
• When larger network properties change, it is because of ego actions
• Lot of interesting work to be done on ego network change
• ENRDs
– Can employ standard sampling techniques
• And so standard statistical methods
– Cheaper & easier to deploy
• Can collect richer data – more ties
• Excellent fit with qualitative/case‐oriented methods
– Fewer privacy/ethical issues
• May improve validity of data

60. Gracias!
Thank you for inviting me!
Jose‐Luis Molina
Pilar Marques
Carlos Lozares and the QUIT