Summary
Summary Social Network Analysis
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Applied Data Science Utrecht University (UU): social networks with a focus on e.g., contagion and diffusion.
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Social NetworksTheoretical representation of context and individual behaviour, thus not considering network:
Network perspective: zoom-out individuals to describe interaction collective phenomena
- e.g., effect of social relations on health outcomes (micro attributes, macro interaction)
Actor (node, vertex): persons, animals, computer, organisation, class
Relation (edge, tie, link): friendship, family, hierarchy, exchange, competition
Undirected (Friendship), directed (Following), directed weighted (Retweet)
Adjacency matrix: table-by-table end (e.g., sender and receiver)
Centralisation: extent to which nodes are concentrated on a single node
- Take standard deviation of degree
1
, Simple versus multi-edges and self-edges
Hypergraph and corresponding bipartite graph
Bipartite and unipartite networks
Degree: in an undirected network is the number of edges connected. In a directed net- work
each node has two degrees: the in-degree is the number of ingoing edges connected to a node
and the out-degree is the number of outgoing edges
Shortest path (geodesic path): the shortest walk between a given pair of nodes, i.e., the walk
that traverses the smallest number of edges
Distance: length of the shortest path in terms of number of edges
(Dis)connected component: different for directed versus undirected (isolations)
Community (clusters, cohesive groups or modules): in which the nodes are more connected
to each other than to the rest of the network (may overlap)
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