NetLearn
Based on the PROLEARN publication database, NetLearn uses social network
analysis and visualisation methods to visualise and analyse learning
networks, find experts, and mine communities.
You can use the PROLEARN Academy publication interface to add your own
publications. You can then see different visualisations of your local
network and discover other researchers and communities with similar
research interests.
Social Network at a Glance
Basically, "a social network consists of a finite set or sets of actors and the relation or the relations defined on them". Social network theory sees the social relation in terms of nodes and ties. The nodes mean the actors within the network; actors can be a single person, a team, group, or a company, etc. The ties mean linkage between a pair of actors (S. Wasserman and K. Faust, 1994).
In order to have a better understanding of networks and identification of the important actors in a social network a measure is needed. Measuring the network location means finding the centrality of the node. There are three most popular centrality measures: degree centrality, closeness centrality, and betweenness centrality. "Degree centrality is the number of nodes that a given node is connected to". Closeness centrality can be defined as the total graph-theoretic distance to all other nodes in the network. Betweeness centrality is if it lies on several shortest paths among other pairs of the nodes (Kreb07).
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