sunbelt xxiv, portorož, 20041 pajek workshop vladimir batagelj andrej mrvar wouter de nooy
TRANSCRIPT
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Sunbelt XXIV, Portorož, 2004 1
Pajek Workshop
Vladimir BatageljAndrej Mrvar
Wouter de Nooy
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Sunbelt XXIV, Portorož, 2004 2
Today’s Program• Introduction to Pajek and social
network analysis
• Analysing large networks with Pajekand fine-tuning layouts
• Discussion and questions
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PART 1
Exploratory Network Analysis
with Pajek
(Published at Cambridge University Press, October 2004)
W. de Nooy, A. Mrvar, V. Batagelj
ž
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Overview• Network data• Vertex attributes
and properties • Cohesive
subgroups:– in simple networks– in signed networks– in valued networks
• Brokerage:– centrality– structural holes– brokerage roles
• Ranking:– prestige– acyclic networks
• Blockmodeling• Networks and time
– repeated measurement
– diffusion– genealogies, citations
• Network analysis and statistics
• Building your own
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Network data• Opening a network in Pajek• Drawing a network in Pajek
– Energizing the layout– Selecting display options– Exporting the sociogram
• Pajek network data– Structure– Store & export from Access
• Example: World trade relations– Imports_manufactures.net
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Vertex attributes and structural properties
• Types of data objects– Partitions: discrete properties– Clusters: 1 class from a partition– Vectors: continuous (numeric) properties– Hierarchies: nested classification– Permutations: reordering (renumbering)
• Visualizing partitions and vectors• Menu structure• Pajek project file
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Cohesive subgroupsin simple networks
• Connectivity• Example: Attiro.paj• Measures:
– Components: weak and strong– k-cores– Cliques, complete subnetworks
• Analytic strategy
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1. Is the netw ork d irected? In fo>N etw ork>G eneral
2b. F ind w eak com ponents
D o c
N et>C om ponents>W eak
om ponents identify subgroups?
2a. F ind com ponents
D o com ponents identify subgroups?
N et>C om ponents>W eak
Fin ish: subgroupsnot found.
3a. F ind -coresk
conta in subgroups?D o k-cores
N et>Partitions>C ore>Input
4a. R em ove vertices of the low est -coresk O perations>Extract from N etw ork>Partition
Fin ish: subgroupsare classes in the
com ponentspartition.
5.5 F ind com ponents (see 2a.)
5a. F ind overlapping com plete subnetw orks
D o subnetw orks identify subgroups?
Select and execute
N ets>Find Fragm ent (1 in 2) >O ptions>Extract Subnetw ork
N ets>Find Fragm ent (1 in 2)>F ind
3b. F ind strong com ponents N et>C om ponents>Strong
D o com ponents identify subgroups?
5b. Sym m etrize the netw orkN et>Transform >Arcs->Edges>A ll
no yes
yes yes
nono
yesno
yes
no
yes
yes
no
4b. F ind overlapping com plete subnetw orks Select
and execute
N ets>Find Fragm ent (1 in 2) >O ptions>Extract Subnetw ork
N ets>Find Fragm ent (1 in 2)>F ind
D o subnetw orks identify subgroups?
no
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Cohesive subgroups in signed networks
• Balanced clusters• Example: Sampson.paj• Using line values & signs in layout• Optimization approach
– Set parameters– Search optimal solution– Repeat many times
• Stepping through partitions
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Cohesive subgroupsin valued networks
• Cohesion by strong or multiple ties• Example: interlocking directorates
in Scottish banking (circa 1900) Scotland.paj
• Transform 2-mode into 1-mode network
• Measure:– m-core (valued core)
• SVG output
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Centrality• Centrality and centralization• Undirected networks (Knoke & Burt,
1983)
• Example: Strike.paj– Degree– Closeness– Betweenness
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Brokerage• The flow of information• Example: Strike.paj• Overall network structure:
– Bridges– Cut-vertices or articulation points– Bi-components
• Investigating the ego-network:– Structural holes– Brokerage roles
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5 Brokerage roles
v
u w
coord inator
v
u w
itinerant broker
v
u w
lia ison
v
u w
gatekeeper
v
u w
representative
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Prestige• Asymmetric choices• Example: SanJuanSur2.paj• Measures:
– Popularity: indegree– Input domain: direct and indirect
nominations– Proximity prestige: size of domain
divided by the average distance within the domain
• Structural and social prestige
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Ranks: acyclic networks• Discrete ranks or levels• Example: student_government.paj• Local network structure:
– Triadic analysis and the triad census
• Overall network structure:– Strong components and ranks– Symmetric-acyclic decomposition
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Balance-theoretic modelsModel Ties within a cluster Ties between ranks Permitted triads
Balance symmetric ties within a
cluster, no ties between
clusters; max 2 clusters
none 102, 300
Clusterability idem
no restriction on the
number of clusters
idem + 003
Ranked
Clusters
idem asymmetric ties from each
vertex to all vertices on
higher ranks
+ 021D, 021U,
030T, 120D, 120U
Transitivity idem null ties may occur
between ranks
+ 012
Hierarchical
Clusters
asymmetric ties within a
cluster allowed provided
that they are acyclic
idem + 120C, 210
no balance-theoretic model (‘forbidden’) 021C, 111D,
111U, 030C, 201
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Triad types and models
1 - 003
2 - 012
3 - 102
4 - 021D
5 - 021U
9 - 030T
12 - 120D
13 - 120U
14 - 120C
15 - 210
16 - 300
Balance C lusterability
Transitiv ity H ierarchica lC lusters
R anked C lusters
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Blockmodeling• Matrix and permutation for visualization• Blockmodel
– Partition of vertices into classes (positions)– Image matrix of relations among blocks
• Types of blockmodels– Cohesive subgroups– Center-periphery structure– Ranks
• Types of equivalence:– Structural equivalence: hierarchical clustering– Regular equivalence
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Cohesive subgroupsD om ingo
C arlosA le jandroEduardo
FrankH al
KarlBobIkeG ill
LannyM ikeJohn
XavierU trecht
N ormR ussQ uint
W endleO zzie
TedSamVernPaul
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Image matrix
Class SpanishEnglish –
youngEnglish –
old
Spanish Complete Empty Empty
English – young
Empty Complete Empty
English – old
Empty Empty Complete
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Blockmodel types
C ohesion C enter-periphery R anking
Im age m atrix
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Regular equivalence and errors
pm inister
m in isters
m inister2
m inister3
m inister4
m inister5
m inister6
m inister7
advisor1
advisor2
advisor3
pm inister advisors
m inister1
X
X
XX
X
X
X
X
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Networks and time• Longitudinal network: a network
measured at different time points– Example: Sampson.paj
• Diffusion: vertex property changing over time, e.g., adoption– Example: ModMath.paj
• Descent: a relation spanning time– Genealogies: descent by birth; structural
relinking– Citations: descent of ideas; main path analysis– Example: Gondola_Petrus.ged,
centrality_literature.paj
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Genealogies• Data format: GEDCOM 5.5 standard
www.gendex.com/gedcom55/55gcint.htm
• Software:- Genealogical Information Manager www.mind spring.com/~dblaine/gim home.html- Personal Ancestral File www.familysearch.org
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Networks and statistics• Statistical relations among properties of
vertices: partitions and vectors• Example: social and structural prestige
(Ch. 9)• In Pajek: discrete (Cramer’s V, Rajski,
rank correlation) and continuous (Pearson correlation, regression)
• Pajek to R: see afternoon session• Pajek to other statistics software: paste
numbers from partition or vector into statistics software datasheet
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Building your own• Macro: sequences of commands
performed on selected data objects
• Example: exposure in a diffusion network
• Macro commands:– Record– Add message: add comment– Play
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Relations among chapters
C h.1 - Looking for socia l structure
C h.2 - A ttributes and re la tions
C h.3 - C ohesive subgroups
C h.4 - Sentim ents and friendship
C h.5 - A ffilia tions
C h.6 - C enter and periphery
C h.7 - B rokers and bridges
C h.8 - D iffusion
C h.9 - P restige
C h.10 - R anking
C h.11 - G enealogies and cita tions
C h.12 - B lockm odels