big data workshop 02 - social network analysis
TRANSCRIPT
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SNA(Social Network Analysis)
09 - 2014
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SNA: Intro
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� Qué [no] es?� Architectura� Cuándo usarlo?...� Herramientas- Gephi� Componentes� Métricas� Casos de uso� Buenas Prácticas� Ejercicios
SNA: Agenda
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SNA: ...no es Sociología
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SNA: Arquitectura
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� Comprender una red social (on/off line) o hacerla más eficiente
� Visualizar de forma gráfica una red y encontrar relaciones ocultas
� Descubrir patrones y caminos de información
SNA: Por qué y Cuándo?
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� NodeXL: http://nodexl.codeplex.com/
� Gephi: https://gephi.github.io/
� NetMiner: http://www.netminer.com/sub02/sub02.php
� Pahek: http://pajek.imfm.si/doku.php
� Cytoscape: http://www.cytoscape.org/
� R with SNA lib: http://cran.r-project.org/
� otros ...
SNA: Herramientas
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SNA: Herramientas - Gephi
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Prácticas
Familiarizarse con Gephi
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� sif� psmi� sbml� GraphML� kgml� GML� XGMML� BioPAX
SNA: Archivos
� cvs� tab� json� xls
� others...
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SNA: Componentes
F
G
E
D
C
B
A
Nodos
Arista
s
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SNA: Métricas - Degree
A
Nodos
Aristas
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SNA: Métricas: Un/Directed Degree
F
G
E
D
C
B
A
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SNA: Métricas - Peso / Ranking / Tipo
D
C
B
A
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SNA: Métricas - Notación de Relaciones
nodos a b c
x 0 1 0
y 1 1 0
z 1 1 1
nodos
x b
y a
y b
z a
z b
z c
nodos
x b
y a,b
z a,b,c
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SNA: Métricas - In/out Degree
A
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B
SNA: Métricas - Componentes Conectados
Fuertemente
vs Débilmente
F
G
E
D
C
B
A
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SNA: Métricas - Giant Component
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Identificación de métricas
Prácticas
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SNA: Métricas - Shortest path
C
F
G
E
D
B
A
A → F
A F
C
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SNA: Métricas - Shortest path
Problema
del
viajante
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Centrality
SNA: Métricas - Centrality
Indegree
OutDegree
Betweenness
Closeness
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Node #
A 3
B 2
C 6
D 2
E 1
F 1
G 1
C
F
G
E
D
B
A
SNA: Métricas: Degree Ranking
C
Node #
A 3
B 2
C 6
D 2
E 1
F 1
G 1
Node #
C 6
A 3
B 2
D 2
E 1
F 1
G 1
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Node #
A
B
C
D
E
F
G
Node #
A 1
B 0
C 12
D 0
E 0
F 0
G 0
C
F
G
E
D
B
A
SNA: Métricas: Betweenness Ranking
Cuán frecuente un nodo aparece en un camino crítico
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Node #
A
B
C
D
E
F
G
Node #
A 1
B 0
C 12
D 0
E 0
F 0
G 0
C
F
G
E
D
B
A
SNA: Métricas: Betweenness Ranking
B
D
11,5
0,5
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Node #
A
B
C
D
E
F
G
Node #
A 0,5
B 0
C 11,5
D 0
E 0
F 0
G 0
C
F
G
E
D
B
A
SNA: Métricas: Betweenness Ranking
H I
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C
F
G
E
D
B
A
Node #
A
B
C
D
E
F
G
SNA: Métricas: Closeness Ranking
Node #
A 1,5
B 1,6
C 1
D 1,6
E 1,83
F 1,83
G 1,83
Node #
A 1,5
B 1,6
C 1
D 1,6
E 1,83
F 1,83
G 1,83
(1+1+1+2+2+2) / 6 = 1,5
Número de saltos por el camino crítico
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CF
G
E
D
B
A
Node #
A
B
C
D
E
F
G
SNA: Métricas: Closeness Ranking
Node #
A 1,5
B 1,6
C 1
D 1,6
E 1,83
F 1,83
G 1,83
Node #
A 1,5
B 1,6
C 1
D 1,6
E 1,83
F 1,83
G 1,83O
(1+1+1+1+1+1) / 6 = 1
N ML
K
JI
H
Hops(N-K): 5 Hops(O-K) = Hops(c) + 1
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SNA: Métricas - Modularidad
C
F
G
E
D
B
A
D
DD
D D
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Quienes lo Usan
y cómo?
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SNA: Quiénes lo usan? cómo?
� Seguros
� Bancos
� Redes Sociales
� Publicidad viral vs spam
� Propagación de virus/parches
� Mejorar comunicación intra-companía
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SNA: Mi red LinkedIn
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SNA: Twitter Influencers
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SNA: Caso Twitter
Algunos datos:~190k tweets2 hashtags
La red:# usuarios: ~66,5k# aristas: ~156k
http://migueldelfresno.com/2014/04/super-influyentes-quienes-son.html
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SNA: Caso Twitter
Giant Component
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SNA: Caso Twitter
Sub-comunities
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SNA: Caso Twitter
Resultado:
< 0,25% de nodos influyentes
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SNA: Twitter Exposure vs Reach
https://blog.tweetreach.com/
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SNA: Compañías de Análisis
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Buenas Prácticas
...algunas
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� Reducir el cruce de aristas
SNA: Buenas Prácticas
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� Aristas homogéneas
SNA: Buenas Prácticas
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� No superponer aristas y nodos
SNA: Buenas Prácticas
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� Conocer dominio y variables
SNA: Buenas Prácticas
� Muestra significativa
� Revisar e invalidar
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Ejercicios
Análisis de mi red de FBhttps://apps.facebook.com/netvizz/
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