Social Network Flashcards
Social Network Analysis
focuses on the social connections as if they are on a map. it helps us to map out social connections, observe patterns and understand how societies function. The basic perspective of network analysis is based on graph theory.
Central people vs. edge people
some people/groups are very central, meaning they’re connected to a lot of others. Others might be on the edges, not connected to many others. it helps us understand, how info, ideas or even things like diseases can spread thru networks, how communities form and evolve, how ideas spread and how power dynamics work within groups.
node
each person, idea or organization represented by node or vertex
edges/links/ties
relationships or connections represented by lines
nodes and edges
singletons (nodes have no edges)
DYAD (has two nodes)
TRIAD (has a group of 3 nodes)
connector hubs
have diverse connections
gatekeeper
node with advantage of access to another cluster
bridge
embedded as part of two clusters and unites them
isolate
node with no or few connections
Three most widely measures are
Degree, closeness, betweenness
identifying a nod we can measure it by calculating centrality. centrality describes how important a node in a particular network
Degree centrality measured by numbers of connections or number of edges
higher the degree, more central the node.
closeness centrality
closeness centrality measures how close a node is to all other nodes in the network it is defined as the reciprocal of the average shortest path distance from a node to all other nodes in the network
purpose: it indicates how quickly info can spread from a node to all others, as a node with high closeness centrality can reach others faster due to shorter path distances.
betweenness centrality
betweenness centrality measures the extent to which a node lies on the shortest paths between other nodes in the network. It is a measure of how much a node acts as a bridge or gatekeeper between other nodes.
Purpose: it highlights nodes that control info flow within the network. Nodes with high betweenness centrality can influence or mediate communication between other nodes.
centralized vs decentralized
a network is centralized if there are only one or two people with a lot of connections and hence a lot of power in the network. A network is decentralized if most of the people are connected to most of the other people.
high clustering vs low clustering
clustering refers to the tendency of nodes in a network to form tightly-knit groups or clusters, where many of the nodes are directly connected to one another. The clustering coefficient measures the degree to which nodes in a network tend to cluster together.