Social Network Analysis:
Principles and Applications
Petr Matous
1
Some literature
2
Basic of the methods
3
Methods bible
Wasserman and
Faust
More
mathematical
4
Understanding the substance
of social networks
Kadushin
5
How to perform SNA in Excel
(focus on social media)
6
Mechanism
of networks
Mark
Granovetter
Highly
influential
7
Management and
beyond
8
Other influential research
and interesting applications
9
The story
behind
groundbreaking
research
10
Christakis
and Fowler
Obesity
spreads
through
networks
???
Extremely
popular
11
12
2
SOCIAL NETWORK
ANALYSIS
PRINCIPLES AND METHODS
13
IN SIMPLE TERMS
14
MAIN SNA
TERMS
I. WHAT IS A
NETWORK?
15
What type of networks can you think of?
I. WHAT IS A
NETWORK?
Computer network (the Internet)
-
Road network
-
…
16
-
Source: Google Maps
I. WHAT IS A
NETWORK?
17
What do all these have in common?
I. WHAT IS A
NETWORK?
Collection of nodes which are interconnected by ties
vertices
edges
links
arc
connections
What is a node,
what is a tie?
•
•
Railway network
Computer network
20
I. WHAT IS A
NETWORK?
I. WHAT IS A
NETWORK?
Source: http://burak-arikan.com/
21
A group of people (represented as nodes),
who are interconnected by some type of relationship (ties)
I. WHAT IS A
NETWORK?
What can be a node in a social network?
Individual
Household, village, city
Organization, department, firm
Source: relenet.com
I. WHAT IS A
NETWORK?
23
What is a tie?
II. TIES
Types of personal
relationships
1. Kinship
2. Friendship (catchall?)
3. Acquaintance
24
4. Professional relationship
II. TIES
Kinship networks
Source:
http://www.iesonava.info/olgaenlar
ed/elena/THE_FAMILY_6.htm
25
Family trees
~ the oldest type
of SN diagrams
II. TIES
1.
Information sharing, advice
2.
Imitation
3.
Influence
4.
Social support, help
5.
Gossip
6.
Borrowing, lending
7.
Romance
8.
Infection transmission
26
All of the following be represented
and analyzed as ties
II. TIES
1.
Inter-firm trade
2.
Contractual relationships among stakeholders in a
project
3.
…
27
Also
II. TIES
Some relationships are relatively strong,
Some are weaker
It is useful to distinguish these
28
But how?
II. TIES
29
How to determine the strength of a tie in a survey?
II.
TIES
Strength
Perceived closeness:
“Rate from 1 to 10 how close you feel to this person.”
← problems with direct questions
Frequency of meeting
“How often do you meet?” ← clear but not always reliable
Length of relationship
“When did you meet this person?” ← clear, sometimes to establish
order of events, but not always reliable
30
Kinship
II. TIES
31
Can name some examples of strong ties?
II. TIES
Examples of strong ties
Good friends
Family members?
Some work colleagues?
32
Some neighbors?
II. TIES
33
Can you name some examples of weak ties?
II. TIES
Examples of weak ties:
Professional relationships
Acquaintances
People you know only through other people
Former friends
34
SNS
II. TIES
Direction of ties
Other
examples?
35
E.g. Information/support goes from one person to another
II. TIES
Direction of ties
36
Examples of
directed
networks?
Building block of SN
37
III. TRIADS
REAL EXAMPLE:
A COMMUNICATION NETWORK IN A
REMOTE JAPANESE VILLAGE
Nearby
households,
same color
The real leader?
Node size ~ “betweenness”
38
The official
village leader
1.
SNA for descriptive & exploratory purposes
– see the patterns
2.
Spring embedding: Position, distance on the screen
39
VISUALIZATION
40
Dynamic visualization (Visone)
EXERCISE
Constructing a network of the people in
this room
41
How?
EXERCISE
Constructing a network of the people in this room
1.
Node?
2.
Tie??
What is the research question?
“Who are the key players for information flows in this group
of people?”
Information network
•
“Knowing”?
•
Any type of communication?
•
Face-to-face communication?
What is a “useful” definition that will help us uncover the
structure??
42
•
43
Name everyone out of the people within
this room,
you had communicated(?) with
prior to this meeting
This exercise is only for illustration
but still need to think about
Sensitivity (privacy)
– asking respondents information about others
•
Respondents’ interaction
2.
Reliability
3.
Unique identification (IDs)
44
1.
4. Roster (related to the previous points, boundary,
identification)
5. Free recall (limit, order)
45
6. Administration
NETDRAW
Martin Everett, Steve Borgatti (Organization Science)
Free
Visualization
Basic Analysis
•
•
•
•
Multiple relations
Valued relation
Node attributes
2-mode data
46
UCINET (Excel data)
47
DATA INPUT
DL files (*.txt)
48
• Full matrix
• Node list
• Edge list
49
dl
n = 50
format = nodelist
data:
1782
3 19 21 49 6
26
VNA FILE
50
Can combine both relational and nodal data
*node data
ID name gender age
j101 joe male 56
w067 wendy female 23
b303 bill male 48
*tie data
from to friends advice
j101 w067 1 3
w067 j101 0 1
j101 b303 1 2
51
w067 b303 0 6
LAYOUT
Position, distance on the screen (- visualization)
•
•
•
•
•
•
Random?
Circle?
Spring embedding
Iterative, “badness of fit”-minimize energy
Minimum crossings
Other conditions, other methods
52
Try on our data
53
54
HOW CAN WE
QUANTITATIVELY
DESCRIBE A NETWORK?
HOW CAN WE
QUANTITATIVELY
DESCRIBE A NETWORK?
1. Individuals
2. Links
55
3. Whole network
56
WHICH INDIVIDUALS
ARE IMPORTANT IN A
NETWORK?
CENTRALITY
Who is “important” in a network?
•
Use (spread of ideas, behavior)
•
•
Disrupt
•
•
Immunize
Arrest, kill
At the network level:
What is the distribution of “importance”?
57
•
Dissemination
58
HYPOTHETICAL
EXAMPLE
DEGREE
E.G. KNOWING MANY
PEOPLE
(may not always be important
- “vanity metric”)
59
Outdegree
x indegree!
BROKERS AND
BRIDGES
High betweenness
Advantages &
disadvantages
“Structural holes”
(possible assess quantitatively, not necessary in this simple
example)
60
“Strength of weak ties”
DENSITY
“EVERYONE KNOWS EACH
OTHER”
“Community” detection
“cliques”
Distinct cultures
in an
organization?
61
~ teams?
EMBEDEDNESS
62
“MY FRIENDS KNOW EACH
OTHER”
NETWORK-LEVEL
COMPARISONS
Exploring overall structure
Size (?)
Number of components (?)
Density (?) (x average degree)
Reciprocity
Degree distributions (“power law”?)
Core & periphery?
How about our network?
EGO AND
PERSONAL
NETWORKS
65
HYPOTHETICAL
EXAMPLE
ADVANTAGES OF THE PERSONAL
NETWORK APPROACH
Not surveying everyone in the network, not connecting their
responses
Can choose sampling based on the
• Research question
• Local context
• Feasibility
66
No need for (potentially artificial) boundaries
DRAWBACKS
Cannot connect the networks
Cannot see the whole structure
Cannot verify reciprocity
Asking about relations to people “anywhere”?
67
1.
2.
3.
WAYS AROUND
68
1. Use proxy for structure
69
WE WANT TO KNOW IF THE
RESPONDENTS’ “FRIENDS” ARE
ALSO CONNECTED
70
BUT IF WE ASK ONLY ONE,
WE GET ONLY THIS
DO YOU THINK YOUR FRIEND “X” AND
YOUR FRIEND “Y” KNOW EACH OTHER?
71
(Do they meet
even without
you?)
WAYS AROUND
72
2. Check if the respondent really knows the other person
WAYS AROUND
3. Getting a sample of the respondent’s personal network
73
• E.g. special type of people, random people
DO YOU KNOW SOMEONE,
WHO IS “E”, “X”, “Y”…?
74
Questions
about the
elicited
individuals
and their
relations
follow.
WAYS AROUND
4. Snowballing
“between person and whole networks”
For unidentified or hard-to-reach populations
Continuing ego network elicitation
in 2-steps or more steps
EGO AND PERSONAL
NETWORK METRICS
Size
Density
Number of weak components
(taking out ego (FB net pics))
Effective network size
(the number of alters that ego has, minus the
average number of ties that each alter has to other
alters)
- related to managers performance
Efficiency = Effective size/ degree
Applicable both to personal
networks and ego networks
77
VISUALIZING AND
ANALYZING YOUR
FACEBOOK
NETWORK
VISUALIZING AND
ANALYZING YOUR
FACEBOOK NETWORK
Realize what your network is like
Practice some SNA concepts in practice
How many groups of “Friends” do you have?
Who are the groups composed of?
1 GRAPH METRICS
Select all
Calculate graph metrics
79
Overall metrics
2 SUBGRAPH IMAGES
80
Vertices
3 GROUPS
Clanset-Newman-Moore (for large networks)
Collapse/expand all/some groups
81
Create groups by attribute
4 VISUALIZATION
Harel-Koren (fast)
(alternate with Fruchtman)
Set repulsion and iterations
Filter out isolates (almost isolates)
Try different attributes for colors (locale)]
Set manually opacity of edges/vertices
82
Experiment with curved, bundled ties
ORIGINAL
RESEARCH
EXAMPLES
PETR MATOUS
83
YASUYUKI TODO
Overall network composition and structure
first-name method (McCarthy et al 1997)
cues to elicit a sample of respondents’
alters
our study
random names from the area
a representative section of
respondents’ networks
+ access to selected key persons
84
Constructing
the battery
of first-names
1. 78 random names
from the HH head list
2. Same number
of male and females
85
Continue until
14 positive answers reached
86
Additional
information
Efficiently obtain basic info
about large number inhabitants (N=4,158)
1. Relationship
2. Walking minute distance (location didn’t work)
3. Main mode of contact
4. Frequency of contact
5. Length of the relationship
6. Occupation
7. Ethnicity
8. Religion
9. Trust questions
87
87
Structure
1.
2.
3.
4.
Random 14 pairs
“Would you say that these two know each other?”
Proportion -> density proxy
What type of alters know each other (total
N=4,158)
A
A
B
D
D
C
B
88
Determinants
of personal network characteristics
(OLS)
Overall SN
size
-active
- passive
Underlying factors
89
Degree
(size)
Education
Education
Education
5-9 yrs
10-15 yrs
0.30*
0.38*
Age Age Age
55-59 yrs 60-64 yrs
0.71*
0.85**
Sex
Sex
(male=1)
0.30*
[units Std Dev]
90
Transitivity
(density)
Education
Education
Education
[Percentage of alters that
know each other]
Age Age Age
Hood
majority
Age
Age
5-9 yrs 10-15 yrs 40-44 yrs 60-64 yrs 65-69 yrs 70-74 yrs
-5.49* -11.03*** -12.74* -15.15* -17.96** -15.65* -10.42**
Proportion of neighbors of the
same ethnicity within 1km radius
91
92
Mobile phone experiment
93
Sending weekly messages about resource-conserving agriculture
(e.g. compost, crop diversification)
to a randomly-selected subgroup of the phone users
94
1
2
3
4
5
6
95
1
2
3
5
6
Casual chats
4
96
1
2
3
5
6
Casual chats
4
97
Stochastic actororiented modeling
• Learning networks highly dynamic!!
• Regularities: clustering and distrusting information from other cliques
• Influence but not selection!
• Networks matter (but not for everything!)
Supply chain networks and resilience to natural disasters
Data: transactions all Japanese companies
Before and after Great East Japan Earthquake
Controlling for firms’ characteristics
and performance before the earthquake:
If company suppliers have many suppliers themselves
→ vulnerability
Many partners
→ resilience
Long distance partners
→ short-term recovery
Short-distance contacts
→ medium-term recovery
Top 500 firms
Most based in Tokyo
Topology X Geography
Energy companies are the core in the network topology
but are on the geographical periphery
Regional disasters → most major firms temporarily affected indirectly
Disaster in Tokyo → most major firms directly affected, support from the
regional energy firms