Nt1310 Unit 1 Exercise 1 Case Study
2.SNA Measures
Ones we have known about nodes in the network and links between them, then to analyze this network some SNA Measures are used. These measures are mathematical aggregation functions which calculates various aspects related to each node and also some of them can calculate some of the aspects with respect to the whole network .
2.1 Degree centrality[1]
Degree centrality shows how much a node connected with other nodes. For a node if more the number of direct connections with other nodes more the Degree centrality will be. Greater Degree centrality supports the chances of node to be active. To calculate Degree centrality let N is the set of all nodes in the network , Degree centrality Cd of any node i then
where (i, j) representing a link between node i and node j. n is the total number of nodes in the network.
In figure 1 node C will contain greatest value and node A will contain least value for Degree centrality.
2.2 Betweenness centrality[1]
Between two nodes in a network there can be many connecting paths but usually shortest path is considered best. It means any information exchange between two nodes also go through all nodes appearing in shortest path. By this reason any node involved in shortest paths can have …show more content…
We cannot underestimate nodes like N because although these kind of nodes may have limited neighbor nodes but these neighbors are powerful. By Eigenvector Centrality this aspect of a node is determined. It is very common tendency of the key player of any criminal activity to maintain the gap and privacy from members with less importance or new members that's why node having higher Eigenvector Centrality may be the key node. If network graph is represented in the form of adjacency matrix A then Eigenvector Centrality
Ones we have known about nodes in the network and links between them, then to analyze this network some SNA Measures are used. These measures are mathematical aggregation functions which calculates various aspects related to each node and also some of them can calculate some of the aspects with respect to the whole network .
2.1 Degree centrality[1]
Degree centrality shows how much a node connected with other nodes. For a node if more the number of direct connections with other nodes more the Degree centrality will be. Greater Degree centrality supports the chances of node to be active. To calculate Degree centrality let N is the set of all nodes in the network , Degree centrality Cd of any node i then
where (i, j) representing a link between node i and node j. n is the total number of nodes in the network.
In figure 1 node C will contain greatest value and node A will contain least value for Degree centrality.
2.2 Betweenness centrality[1]
Between two nodes in a network there can be many connecting paths but usually shortest path is considered best. It means any information exchange between two nodes also go through all nodes appearing in shortest path. By this reason any node involved in shortest paths can have …show more content…
We cannot underestimate nodes like N because although these kind of nodes may have limited neighbor nodes but these neighbors are powerful. By Eigenvector Centrality this aspect of a node is determined. It is very common tendency of the key player of any criminal activity to maintain the gap and privacy from members with less importance or new members that's why node having higher Eigenvector Centrality may be the key node. If network graph is represented in the form of adjacency matrix A then Eigenvector Centrality