For Dummies Yanne Broux DH Summer School Leuven September 8 2015 Terminology Useful sources AL Barabási Linked The Science of Networks Cambridge 2002 S Borgatti et al ID: 524607
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Slide1
Social Network Analysis
For Dummies
Yanne
Broux
DH Summer School
Leuven, September 8 2015Slide2
TerminologySlide3
Useful sources
A.-L.
Barabási
,
Linked: The Science of Networks
(Cambridge, 2002)
S.
Borgatti
et al.,
Analyzing Social Networks
(L.A., 2013)
Y.
Broux
& S.
Vanbeselaere
,
Six Degrees of Spaghetti Monsters
(spaghetti-
os.blogspot.com
)Slide4Slide5
Basics
Node (vertex)
Edge
(tie)
Undirected
Directed
Weighted (valued)Degree: how many edges to a nodeUndirected: count edgesDirected: indegree vs outdegree
A
B
C
D
E
FSlide6
Data managementSlide7
Adjacency
matrix
Symmetric
,
binary
e.g.
who knows who Symmetric, weighted
e.g. distance between placesSlide8
Adjacency
matrix
Asymmetric
,
binary
e.g. choose 3 friends to sit withAsymmetric, weighted
e.g. number of emails sent to colleaguesSlide9
One
-mode
vs
two
-mode
1-mode: direct ties between actors (= adjacency matrix)2-mode: ties between different entities (= affiliation matrix)Slide10
Adjacency
vs
attribute
matrix
Adjacency matrix: only records ties between nodesAttribute matrix: each column is different attribute of the nodes (gender, role, ethnicity, status, …) = ‘nodelist’ (vs ‘
edgelist’)Slide11
Attribute matrix (nodelist)