Social Network Analysis - Massachusetts Institute of Technology
Transcript of Social Network Analysis - Massachusetts Institute of Technology
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Social Network Analysis
Basic Concepts, Methods & Theory
University of Cologne Johannes Putzke
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Agenda
Introduction
Basic Concepts
Mathematical Notation
Network Statistics
2
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Textbooks
Hanneman & Riddle (2005) Introduction to Social Network Methods,
available at http://faculty.ucr.edu/~hanneman/nettext/
Wasserman & Faust (1994): Social Network Analysis – Methods and
Applications, Cambridge: Cambridge University Press.
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Folie: 4
Introduction
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Basic Concepts
What is a network?
University of Cologne Johannes Putzke
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What is a Network?
Actors / nodes / vertices / points
Ties / edges / arcs / lines / links
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What is a Network?
Actors / nodes / vertices / points
Computers / Telephones
Persons / Employees
Companies / Business Units
Articles / Books
Can have properties (attributes)
Ties / edges / arcs / lines / links
Folie: 7
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What is a Network? Actors / nodes / vertices / points
Ties / edges / arcs / lines / links connect pair of actors
types of social relations friendship
acquaintance
kinship
advice
hindrance
sex
allow different kind of flows messages
money
diseases
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What is a Social Network? - Relations among People
Folie: 9
Ken George
Kim Peter
Juan
Lee Stanley
Rob
Kai
Johannes
Steve Paul
John
Patrick
Homer
Mike
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What is a Network? - Relations among Institutions
as institutions
owned by, have partnership / joint venture
purchases from, sells to
competes with, supports
through stakeholders
board interlocks
Previously worked for
Folie: 10
6%
7%27%
22%
8%
51%10%12%
9% 13%
14%
51%
16%
100%
100%9%
7%
32%
15%
16%
42%
72%
23%
Image by MIT OpenCourseWare.
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Why study social networks?
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Example 2) Homophily Theory I I
I
age / gender network
Birds of a feather flock together
See McPherson, Smith-Lovin & Cook (2001)
Folie: 12
Male Female Male 123 68
Female 95 164
0-13 14-29 30-44 45-65 >65 0-13 212 63 117 72 91 14-29 83 372 75 67 84 30-44 105 98 321 214 117 45-65 62 72 232 412 148 >65 90 77 124 153 366
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Managerial Relevance – Social Network…
Folie: 13
Shaneeka
JoseJuan
Mitch
Ashley
Pamela Jody
Alisha
AshtonBill
Callie Andy
CodySean
Ben
Ewelina
Nichole Burke
Stock
Brandy
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…vs. Organigram
Folie: 14 Source: http://www.robcross.org/sna.htm
Exploration & Production
ExplorationCody
Drilling Ben
ProductionStock
Mitch
Bill
Ashton
Ewelina Jose
Brandy
Sean
Pamela
Jody
Nichole
Alisha
Callie
G&G
Andy
PetrophysicalAshley
ProductionShaneeka
ReservoirJuan
Senior Vice President Burke Shaneeka
JoseJuan
Mitch
Ashley
Pamela Jody
Alisha
Ashton
Bill
Callie Andy
CodySean
Ben
Ewelina
Stock
Brandy
Nichole Burke
Image by MIT OpenCourseWare.
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SNA – A Recent Trend in Social Sciences Research
Keyword search for„social“ + „network“ in 14 literature databases
Folie: 15
1965 1970 1975 1980 1985 1990 1995 2000 2005 2010
YEAR
0
2000
4000
6000
8000
Abstracts
Titles
Source: Knoke, David
(2007) Introduction to
Social Network Analysis
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SNA – A Recent Trend in IS Research
Folie: 16
020406080100120140160180200
Art
ikela
nzahl
Jahr
EBSCO
ACM
ScienceDirect
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How to analyze Social Networks?
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Example: Centrality Measures
Who is the most prominent?
Who knows the most actors?
(Degree Centrality)
Who has the shortest distance to the other actors?
Who controls knowledge flows?
...
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Example: Centrality Measures
Who is the most prominent?
Who knows the most actors?
Who has the shortest distance to the other actors? (Closesness Centrality)
Who controls knowledge flows?
...
Folie: 19
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Example: Centrality Measures
Who is the most prominent?
Who knows the most actors
Who has the shortest distance to the other actors?
Who controls knowledge flows?
(Betweenness Centrality)
...
Folie: 20
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Folie: 21
Basic Concepts
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Dyads, Triads and Relations
friendship
kinship
actor
dyad
triad
relation: collection of specific ties among
members of a group
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Strength of a Tie
Social network finite set of actors and
relation(s) defined on them
depicted in graph/ sociogram
labeled graph
Strength of a Tie dichotomous vs.
valued
depicted in valued graph or signed graph (+/-)
Folie: 23
5 2
Ken, male, 34
Anna, female, 27
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Strength of a Tie
adjacent node to/from
incident node to
Strength of a Tie nondirectional vs.
directional
depicted in directed graphs (digraphs)
nodes connected by arcs
3 isomorphism classes
null dyad
mutual / reciprocal / symmetrical dyad
asymmetric / antisymmetric dyad
converse of a digraph
reverse direction of all arcs
Folie: 24
+ -
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Walks, Trails, Paths (Directed) Walk (W)
sequence of nodes and lines starting and ending with (different) nodes (called origin and terminus)
Nodes and lines can be included more than once
Inverse of a (directed) walk (W-1) Walk in opposite order
Length of a walk How many lines occur in the walk? (same line counts double, in
weighted graphs add line weights)
(Directed) Trail Is a walk in which all lines are distinct
(Directed) Path Walk in which all nodes and all lines are distinct
Every path is a trail and every trail is a walk
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Walks, Trails and Paths - Repetition
W = n1 l1 n2 l2 n3 l4 n5 l6 n6
n1
n3
W = n1 l1 n2 l2 n3 l4 n5 l4 n3
W = n1 l1 n2 l2 n3 l4 n5 l5 n4 l3 n3
Path
origin
terminus
Walk
Trail
University of Cologne Johannes Putzke
Folie: 26
l1
l2
l3
l4 l5
l6 l7
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Reachability, Distances and Diameter
Reachability If there is a path between
nodes ni and nj
Geodesic Shortest path between two nodes
(Geodesic) Distance d(i,j) Length of Geodesic (also called „degrees of separation“)
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Mathematical Notation and Fundamentals
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Three different notational schemes
1. Graph theoretic
2. Sociometric
3. Algebraic
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1. Graph Theoretic Notation N Actors {n1, n2,…, ng}
n1 nj there is a tie between the ordered pair <ni, nj>
n1 nj there is no tie
(ni, nj) nondirectional relation
<ni, nj> directional relation
g(g-1) number of ordered pairs in <ni, nj> directional network
g(g-1)/2 number of ordered pairs in nondirectional network
L collection of ordered pairs with ties {l1, l2,…, lg}
G graph descriped by sets (N, L)
Simple graph has no reflexive ties, loops
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Valued, directed
2. Sociometric Notation - From Graphs to (Adjacency/Socio)-Matrices
Binary, undirected
Folie: 31
I II III IV V VI I II III IV V VI
I II III IV V VI I - 1 II 1 - 1 III 1 - 1 1 IV 1 - 1 1 V 1 1 - 1 VI 1 1 -
symmetrical
I II III IV V VI I II III IV V VI
I II III IV V VI I 2 0 0 0 0 II 0 0 4 0 0 0 III 0 3 0 5 4 0 IV 0 0 5 0 0 0 V 0 0 0 2 0 3 VI 0 0 0 1 4 0
5
IV
I II
III
V VI
IV
I II
III
V VI
3
4
2
5
4 2
1
3
4
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2. Sociometric Notation
X g × g sociomatrix on a single relation
g × g × R super-sociomatrix on R relations
XR sociomatrix on relation R
Xij(r) value of tie from ni to ni (on relation χr) where i ≠ j
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2. Sociometric Notation – From Matrices to Adjacency Lists and Arc Lists
University of Cologne Johannes Putzke
Folie: 33
- 1 1 VI 1 - 1 1 V 1 1 - 1 IV
1 1 - 1 III 1 - 1 II
1 - I VI V IV III II I
I II II I III III II IV V IV III V VI V III V VI VI IV V
Adjacency List
I II II I II III III II III IV III V IV III IV V IV VI V III V IV V VI VI IV VI IV
Arc List
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Network Statistics
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Different Levels of Analysis
Actor-Level
Dyad-Level
Triad-Level
Subset-level (cliques / subgraphs)
Group (i.e. global) level
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Measures at the Actor-Level:
Measures of Prominence: Centrality and Prestige
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Degree Centrality Who knows the most actors?
(Degree Centrality)
Who has the shortest distance to the other actors?
Who controls knowledge flows?
...
Folie: 37
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Degree Centrality I Indegree dI(ni)
Popularity, status, deference, degree prestige
Outdegree dO(ni) Expansiveness
Total degree ≡ 2 x number of edges
Folie: 38
IV
I II
III
V VI
I II III IV V VI I 1 0 0 0 0 II 0 0 1 0 0 0 III 0 1 0 1 1 0 IV 0 0 1 0 0 0 V 0 0 0 1 0 1 VI 0 0 0 1 0 1 Marginals of
adjacency matrix 2 2 1 3 1 1
( )g
DO i o i ij i
j
C n d n x x
2 1 3 2 2 0
( )g
DI i I i ji i
j
C n d n x x
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Degree Centrality II Interpretation: opportunity to (be) influence(d)
Classification of Nodes Isolates
dI(ni) = dO(ni) = 0
Transmitters
dI(ni) = 0 and dO(ni) > 0
Receivers
dI(ni) > 0 and dO(ni) = 0
Carriers / Ordinaries
dI(ni) > 0 and dO(ni) > 0
Standardization of CD to allow comparison across networks of different sizes: divide by ist maximum value
Folie: 39
' ( )1
i
D i
d nC n
g
IV
I
II
III V
VI VII
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Closeness Centrality Who knows the most
actors?
Who has the shortest distance to the other actors? (Closesness Centrality)
Who controls knowledge flows?
...
Folie: 40
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Closeness Centrality Index of expected arrival
time
Standardize by multiplying (g-1)
Problem: Only defined for connected graphs
Folie: 41
IV
I II
III
V VI
VI V IV III II I
VI V IV III II I
- 1 1 2 3 4 VI 1 - 1 1 2 3 V 1 1 - 1 2 3 IV 2 1 1 - 1 2 III 3 2 2 1 - 1 II 4 3 3 2 1 - I VI V IV III II I
11 8 8 7 9
13 Reciprocal of marginals
of geodesic distance matrix
1
1( )
,C i g
i j
j
C n
d n n
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Proximity Prestige
Ii/(g-1)
number of actors in the influence domain of ni
normed by maximum possible number of actors in influence domain
Σd(nj,ni)/ Ii
average distance these actors are to ni
Folie: 42
1
/ ( 1)( )
, /
iP i g
j i i
j
I gP n
d n n I
19
1 2
4 3 6
5
9 8
7
10 11 12
14
17 18
13 15
16
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Eccentricity / Association Number
Largest geodesic distance between a node and any other node
maxj d(i,j)
Folie: 43
19
1 2
4 3 6
5
9 8
7
10 11 12
14
17 18
13 15
16
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Betweenness Centrality Who knows the most actors?
Who has the shortest distance to the other actors?
Who controls knowledge flows?
(Betweenness Centrality)
...
Folie: 44
19
1 2
4 3 6
5
9 8
7
10 11 12
14
17 18
13 15
16
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Betweenness Centrality
How many geodesic linkings between two actors j and k contain actor i? gjk(ni)/gjk probability that distinct actor ni „involved“
in communication between two actors nj and nk
standardized by dividing through (g-1)(g-2)/2
Folie: 45
( )
jk i
j k
B i
jk
g n
C ng
19
1 2
4 3 6
5
9 8
7
10 11 12
14
17 18
13 15
16
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Several other Centrality Measures …beyond the scope of this lecture
Status or Rank Prestige, Eigenvector Centrality also reflects status or prestige of people whom actor is linked
to
Appropriate to identify hubs (actors adjacent to many peripheral nodes) and bridges (actors adjacent to few central actors)
attention: more common, different meaning of bridge!!!
Information Centrality
see Wasserman & Faust (1994), p. 192 ff.
Random Walk Centrality see Newman (2005)
Folie: 46
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Condor – Betweenness Centrality
Folie: 47
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Creator Guru
provides the overall
vision and guidance
“Salesman”
Knowledge Expert
serves as the
ultimate source of
explicit knowledge
“Maven”
Collaborator Expediter
coordinates and
organizes tasks
Communicator Ambassador
links to external
networks
“Connector”
“Gatekeeper”
Sender (+1)
Contribution index
Contribution
frequency
Receiver (-1)
receivedmessa g essen tmessa g es
receivedmessa g essen tmessa g es
__
__
(Actor) Contribution Index
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Measures at the Group-(Global-)Level and Subgroup-Level
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Diameter of a Graph and Average Geodesic Distance
Diameter
Largest geodesic distance between any pair of nodes
Average Geodesic Distance
How fast can information get transmitted?
Folie: 50
19
1 2
4 3 6
5
9 8
7
10 11 12
14
17 18
13 15
16
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Density
Proportion of ties in a graph
High density (44%)
Low density (14%)
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Density
Folie: 52
( -1) / 2
2
L L
gg g
( -1) / 2
L
g g
1 1
( -1)
g g
ij
i j
x
g g
In valued directed graph: Average strength of the arcs
In undirected graph:
Proportion of ties
IV
I II
III
V VI
IV
I II
III
V VI
3
4
2
5
4 2
1
3
4
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Group Centralization I How equal are the individual actors‘ centrality
values?
CA(ni*) actor centrality index
CA(n*) maxiCA(ni*)
sum of difference between largest value and observed values
General centralization index:
Folie: 53
*
1
g
A A i
i
C n C n
*
1
*
1
max
g
A A i
iA g
A A i
i
C n C n
C
C n C n
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Group Centralization II
Folie: 54
*
1
( 1)( 2)
g
D D i
iD
C n C n
Cg g
' * '
1
( 1)( 2) (2 3)
g
C C i
iC
C n C n
Cg g g
* ' * '
1 1
2( 1) ( 2) ( 1)
g g
B B i B B i
i i
C n C n C n C n
CBg g g
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Condor – Group Centralization
Folie: 55
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Subgroup Cohesion
average strength of ties within the subgroup divided by average strength of ties that are from subgroup members to outsiders
>1 ties in subgroup are stronger
Folie: 56
( 1)
( )
s s
s s
iji N j N
s s
iji N j N
s s
x
g g
x
g g g
IV
I II
III
V VI
3
4
2
5
4
2 1
3
4
IV
V VI
21
4
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Folie: 57
Connectivity of Graphs and Cohesive Subgroups
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Connectivity of Graphs
Folie: 58
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Connected Graphs, Components, Cutpoints and Bridges
Connectedness A graph is connected if there
is a path between every pair of nodes
Components Connected subgraphs in a
graph
Connected graph has 1 component
Two disconnected graphs are one social network!!!
Folie: 59
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Connected Graphs, Components, Cutpoints and Bridges
Connectivity of pairs of nodes and graphs Weakly connected
Joined by semipath
Unilaterally connected
Path from nj to nj or from nj to nj
Strongly connected
Path from nj to nj and from nj to nj
Path may contain different nodes
Recursively Connected
Nodes are strongly connected and both paths use the same nodes and arcs in reverse order
Folie: 60
n4 n2 n3 n1
n4 n2 n3 n1
n4 n2 n3
n1
n4 n2 n3 n1
n5 n6
n4 n2 n3 n1
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Connected Graphs, Components, Cutpoints and Bridges
Cutpoints number of components in
the graph that contain node nj is fewer than number of components in subgraphs that results from deleting nj from the graph
Cutsets (of size k) k-node cut
Bridges / line cuts Number of
components…that contain line lk
Folie: 61
19
1 2
4 3 6
5
9 8
7
10 11 12
14
17 18
13 15
16
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Node- and Line Connectivity How vulnerable is a graph to removal of nodes or lines?
Point connectivity /
Node connectivity Minimum number of k for
which the graph has a k-node cut
For any value <k the graph is k-node-connected
Line connectivity / Edge connectivity
Minimum number λ for which for which graph has a λ-line cut
Folie: 62
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Cohesive Subgroups
Folie: 63
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Cohesive Subgroups, (n-)Cliques, n-Clans, n-Clubs, k-Plexes, k-Cores
Cohesive Subgroup Subset of actors among there are relatively strong, direct, intense,
frequent or positive ties
Complete Graph All nodes are adjacent
Clique Maximal complete subgraph of three or more nodes
Cliques can overlap
{1, 2, 3}
{1, 3, 4}
{2, 3, 5, 6}
Folie: 64
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Cohesive Subgroups, (n-)Cliques, n-Clans, n-Clubs, k-Plexes, k-Cores
n-clique maximal subgraph in which d(i,j) ≤ n for all ni, nj
2: cliques: {2, 3, 4, 5, 6} and {1, 2, 3, 4, 5}
intermediaries in geodesics do not have to be n-clique members themselves!
n-clan
n-clique in which the d(i,j) ≤ n for the subgraph of all nodes in the n-clique
2-clan: {2, 3, 4, 5, 6}
n-club
maximal subgraph of diameter n
2-clubs: {1, 2, 3, 4} ; {1, 2, 3, 5} and {2, 3, 4, 5, 6}
Folie: 65
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Cohesive Subgroups, (n-)Cliques, n-Clans, n-Clubs, k-Plexes, k-Cores
Problem: vulnerability of n-cliques
k-plexes maximal subgraph in which each node is adjacent to not fewer
than gs-k nodes („maximal“: no other nodes in subgraph that also have ds(i) ≥ (gs-k) ]
k-cores subgraph in which each node is adjacent to at least k other nodes
in the subgraph
Folie: 66
maximal subgraph in which each node is adjacent to not fewer maximal subgraph in which each node is adjacent to not fewer
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Folie: 67
Analyzing Affiliation Networks
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Affiliation Matrix, Bipartite Graph and Hypergraph, Rate of Participation, Size of Events
Two-mode network / affiliation network / membership network / hypernetwork
nodes can be partitioned in two subsets
N (for example g persons)
M (for example h clubs) depicted in Bipartite Graph
lines between nodes belonging
to different subsets
Folie: 68
Peter
Pat
Jack
Ann
Kim
Mary
Football Ballet Tennis
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Affiliation Matrix, Bipartite Graph and Hypergraph, Rate of Participation, Size of Events
Affiliation Matrix (Incidence Matrix) Connections among members of one of the modes based on linkages
established through second mode
g actors, h events
A= {aij} (g×h)
Folie: 69
Actor
Event
1 1 Mary 1 1 Kim 1 1 Ann
1 Jack 1 Pat
1 1 Peter Tennis Ballet Football
2 2 2 1 1 1
rate of participation
4 3 3 size of event
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Affiliation Matrix, Bipartite Graph and Hypergraph, Rate of Participation, Size of Events
Sociomatrix [ (g+h) × (g+h) ]
Folie: 70
Peter Pat Jack Ann Kim Mary Football Ballet Tennis Peter - 0 0 0 0 0 1 0 1 Pat 0 - 0 0 0 0 0 1 0
Jack 0 0 - 0 0 0 1 0 0 Ann 0 0 0 - 0 0 0 1 1 Kim 0 0 0 0 - 0 1 0 1 Mary 0 0 0 0 0 - 0 1 1
Football 1 0 1 0 1 0 - 0 0 Ballet 0 1 0 1 0 1 0 - 0 Tennis 1 0 0 1 1 1 0 0 -
- 0 0 0 0 00 - 0 0 0 00 0 - 0 0 00 0 0 - 0 00 0 0 0 - 00 0 0 0 0 -
- 0 00 - 00 0 -
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Affiliation Matrix, Bipartite Graph and Hypergraph, Rate of Participation, Size of Events
Homogenous pairs and heterogenous pairs
XrN (g×g), Xr
M (h×h), XrN,M(g×h), Xr
N,M(h×g)
One-mode sociomatrices XN [and XM]
rows, colums: actors [events];
xij: co-membership [number of actors in both events] (main diagonal meaningful, e.g. total events attended by an actor)
Folie: 71
Peter Pat Jack Ann Kim Mary Peter 2 0 1 1 2 1 Pat 0 1 0 1 0 1
Jack 1 0 1 0 1 0 Ann 1 0 0 2 1 1 Kim 2 0 1 1 2 1 Mary 1 0 0 1 1 2
Peter
Pat
Jack
Ann
Kim
Mary
Football Ballet Tennis
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Affiliation Matrix, Bipartite Graph and Hypergraph, Rate of Participation, Size of Events
Event Overlap / Interlocking Matrix
Folie: 72
4 1 2 Tennis 1 3 0 Ballet 2 0 3 Football
Tennis Ballet Football
Peter
Pat
Jack
Ann
Kim
Mary
Football Ballet Tennis
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Cohesive Subsets of Actors or Events
clique at level c (cf. also k-plexes, n-cliques etc.)
subgraph in which all pairs of events share at least c members
connected at level q
subset in which all actors in the path are co-members of at least q+1 events
Folie: 73
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Folie: 74
…HOLDOUT I…
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When is Which Centrality Measure Appropriate?
Folie: 75
Source: Borgatti, Stephen P. (2005) Centrality and
Network Flow, Social Networks 27, p. 55-71
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Assumptions of Centrality Measures
Which things flow through a network and how do they flow?
Folie: 76
Source: Borgatti, Stephen P. (2005) Centrality and
Network Flow, Social Networks 27, p. 55-71
Transfer Serial Parallel
Walks Money exchange
Emotional support
Attitude influencing
Trails Used Book Gossip E-mail broadcast
Paths Mooch Viral infection Internet name-server
Geodesics Package Delivery
Mitotic reproduction <no process>
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Assumptions of Centrality Measures
Example: Betweenness Centrality
Information travels along the shortest route
Probability of all geodesics being chosen is equal
Folie: 77
Transfer Serial Parallel
Walks Random Walk Betweenness ?
Closeness Degree
Eigenvector
Trails ? ? Closeness
Degree
Paths ? ? Closeness
Degree
Geodesics Closeness Betweenness
Closeness ?
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Adequacy of Centrality Measures
Folie: 78
Source: Borgatti, Stephen P. (2005) Centrality and
Network Flow, Social Networks 27, p. 55-71
Transfer Serial Parallel
Walks Money exchange
Emotional support
Attitude influencing
Trails Used Book Gossip E-mail broadcast
Paths Mooch Viral infection Internet name-server
Geodesics Package Delivery
Mitotic reproduction <no process>
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How to Calculate Geodesic Distance Matrices?
Folie: 79
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From Adjacency Matrices to (Geodesic) Distance Matrices I – (Reachability)
Repetition: Matrix Multiplication
XY = Z
zij = xin ynj
Folie: 80
2 4 4 1 3 2
2 4 1 2 0 3
23 14 13 14
X Y Z
g × h h × k g × k
1
h
n
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From Adjacency Matrices to (Geodesic) Distance Matrices II – (Reachability)
X
Power Matrix: Multiplying adjacency matrices
Folie: 81
I II III IV V VI
I 0 1 0 0 0 0
II 1 0 1 0 0 0
III 0 1 0 1 1 0
IV 0 0 1 0 1 1
V 0 0 1 1 0 1
VI 0 0 0 1 1 0
I II III IV V VI
I 0 1 0 0 0 0
II 1 0 1 0 0 0
III 0 1 0 1 1 0
IV 0 0 1 0 1 1
V 0 0 1 1 0 1
VI 0 0 0 1 1 0
2 1 1 2 0 0 VI
1 3 2 1 1 0 V
1 2 3 1 1 0 IV
2 1 1 3 0 1 III
0 1 1 0 2 0 II
0 0 0 1 0 1 I
VI V IV III II I
IV
I II
III V VI
• xikxkj =1 only if lines (ni,nk) and (nk,nj) are present, i.e. X[2,3,4] counts the number of walks
(ninknj) of length 1 [2,3,4] between nodes ni and nj
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From Adjacency Matrices to (Geodesic) Distance Matrices II – (Reachability)
• xij>0 ? two nodes can be connected by paths of length ≤ (g-1)
• Calculate X[Σ] = X + X2 + X3 + … + Xg-1
• X[Σ] shows total number of walks from ni to ni
Folie: 82
IV
I II
III
V VI
I II III IV V VI
I 1 0 1 0 0 0
II 0 2 0 1 1 0
III 1 0 3 1 1 2
IV 0 1 1 3 2 1
V 0 1 1 2 3 1
VI 0 0 2 1 1 2
X2
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From Graphs to (Geodesic Distance)-Matrices (Reachability) – Geodesic Distance
observer power matrices
first power p for which the (i,j) element is non-zero gives the shortest path
d(i,j) = minp xij[p] > 0
Folie: 83
I II III IV V VI
I 0 1 0 0 0 0
II 1 0 1 0 0 0
III 0 1 0 1 1 0
IV 0 0 1 0 1 1
V 0 0 1 1 0 1
VI 0 0 0 1 1 0
I II III IV V VI
I 1 0 1 0 0 0
II 0 2 0 1 1 0
III 1 0 3 1 1 2
IV 0 1 1 3 2 1
V 0 1 1 2 3 1
VI 0 0 2 1 1 2
X2 X
IV
I II
III
V VI
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From Graphs to (Geodesic Distance)-Matrices (Reachability) – Geodesic Distance
Binary, undirected
Folie: 84
IV
I II
III
V VI
VI V IV III II I
VI V IV III II I
- 1 1 2 3 4 VI 1 - 1 1 2 3 V 1 1 - 1 2 3 IV 2 1 1 - 1 2 III 3 2 2 1 - 1 II 4 3 3 2 1 - I VI V IV III II I
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