What is social
network analysis?
As a motto said, it does not matter what you know, but
who you know.
Social network
analysis is the study of the pattern of
interaction between actors. Actors can be people, community, group,
organization, country and so on. The phenomena and data reflected from the
relationship models of these actors are focus of social network analysis. In the
view of social network, the interaction between actors can be presented as a
models or rules based on relationship, and the regular pattern of the relationship
reflect the social structure, so the structure of the quantitative analysis is
the start point of social network analysis.
Therefore, social network analysis focuses on the
social actor and the relationship between the actors, as Fig 1 shows. The nodes
in the network are the people and groups while the links show relationships or
flows between the nodes. Social network analysis provides both a visual and a
mathematical analysis of human relationships.
Fig 1
In the sociogram, measuring the network location is finding the
centrality of a node. These measures help us to find the various roles and
grouping in a network, such as who are the connectors, leaders, bridges, isolates,
where are the clusters and who is in them, who is in the core of the network
and who is on the periphery?
Fig 2
Look at the social network in Fig 2. Two nodes are connected if they
interact in some way. We will use this network to explain three most popular
individual centrality measures: Degree Centrality, Closeness Centrality and Betweenness
Centrality.
Degree Centrality:
Degree centrality is the sum of all other actors who are directly
connected to the actor in concern. In Fig 2, D has the most direct connections
in the network, indicating that D is the most active node in the network. May be
we would like say the more connections, the better, especially in
personal network. But it is not always so. What really matters is where
those connections leads to not how many the connections are. In Fig 2, D
only connects to others in her clique where those are already connected to each
other.
Closeness Centrality:
Closeness centrality represents the mean of the geodesic distances
between some particular node and all other nodes connected with in. It can help
to understand as how long does it take for a message to spread inside the
network from a particular node. In Fig 2, although C and F have fewer
connections than D, the closeness centrality of them are higher than D, that is
say the pattern of their ties allow them to access all the nodes in the network
more quickly than D. Therefore, they are in a great position to get the best
visibility into what is happening in the network.
Betweenness Centrality:
Betweenness centrality counts the number of shortest paths between I and
k that actor j resides on. In the Fig 2, H has few direct connections, even
fewer than the average in the network. But, H is at one of the best locations
in the network——between
two important areaes. H plays a ‘broker’ or ‘bridge’ role in the network. A node
with high betweenness is able to act as a gatekeeper controlling the flow of
resources.
It is just a brief introduction of social network analysis. That’s all~
