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Social Network Analysis Essay

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Functionality in SNA(Social Network Analysis)[7]

Functionalities are firstly the visualization of the network, secondly the computation of statistics based on nodes and on edges, and finally, community detection (or clustering)

1)Visualization of the network- Methods
1) FruchtermanReingold
2) Kamada-Kawai (which has a faster convergence than FruchtermanReingold, but which often does not give so good results than this last one)

2) Computation of statistics based on nodes

A) Vertex and edge scoring
The place of a given actor in the network can be described using measures based on vertex scoring. Common types of vertex scoring are the centrality measures. Within graph theory and network analysis, there are various measures of the centrality of a vertex to determine the relative importance of this vertex within the graph
• Degree centrality
• Closeness centrality
• Between’s centrality
Vertices that occur on many shortest paths between other vertices have higher between’s than those that do not.

PageRank: The score computed by Page Rank is higher for nodes that are highly connected and connected with nodes that are highly connected themselves.

HITS algorithm: Hyperlink-Induced Topic Search (HITS, also known as hubs and authorities) calculates two scores: hub and authority score. The more a vertex has outgoing arcs, the higher is its hub score. The more a vertex has incoming links, the higher is its authority score.

Tools for SNA[7,8,9,10]

a) Pajek graph file

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