Sentinel Visualizer integrates Social Network Analysis (SNA) directly into your link chart diagrams so you can quickly generate SNA metrics on your data. Generate the raw data and see it visually without programming!
Some of the key concepts of Network Metrics come from the field of Social Network Analysis (SNA). SNA provides a set of methodologies and formulas for calculating a variety of criteria that map and measure the links between things. Using Social Network Analysis, you can get answers to questions like:
SNA provides a rich set of metrics, many of which are used in the Sentinel Visualizer Network Metrics functionality.
Degree centrality is the number of direct relationships of an entity.
An entity with high degree centrality:
In our example network diagram above, Alice has the highest degree centrality, which means that she is quite active in the network. However, she is not necessarily the most powerful person because she is only directly connected within one degree to people in her clique—she has to go through Rafael to get to other cliques.
Betweenness centrality identifies an entity's position within a network in terms of its ability to make connections to other pairs or groups in a network.
An entity with a high betweenness centrality generally:
In the example, Rafael has the highest betweenness because he is between Alice and Aldo, who are between other entities. Alice and Aldo have a slightly lower betweenness because they are essentially only between their own cliques. Therefore, although Alice has a higher degree centrality, Rafael has more importance in the network in certain respects.
Closeness centrality measures how quickly an entity can access more entities in a network.
An entity with a high closeness centrality generally:
As with the betweenness example, Rafael has the highest closeness centrality because he can reach more entities through shorter paths. As such, Rafael's placement allows him to connect to entities in his own clique, and to entities that span cliques.
Note: If the network contains any entities that are un-linked (i.e. not linked to any other entities), the Closeness value for all entities in the network is 0. This is due to formulas and algorithms established in Social Network Analysis.
Eigenvalue measures how close an entity is to other highly close entities within a network. In other words, Eigenvalue identifies the most central entities in terms of the global or overall makeup of the network.
A high Eigenvalue generally:
In this example, we can see that Alice and Rafael are closer to other highly close entities in the network. Bob and Frederica are also highly close, but to a lesser value.
Entities that many other entities point to are called Authorities. In Sentinel Visualizer, relationships are directional—they point from one entity to another. If an entity has a high number of relationships pointing to it, it has a high authority value, and generally:
Hubs are entities that point to a relatively large number of authorities. They are essentially the mutually reinforcing analogues to authorities. Authorities point to high hubs. Hubs point to high authorities. You cannot have one without the other.
Take a look at the following network diagram. From a visual standpoint, some clusters and centrality are visible. But the density of information makes it difficult to see all the centrality aspects.
Sentinel Visualizer makes Social Network Analysis available with just a single click of a button. Press the refresh button and the Social Network Analysis Metrics are calculated for every item on the diagram.
Now you can see centrality measures that clearly show the most central nodes in the network. You can sort by any Social Network Analysis number, or click on any node to find its place in the network.
You can apply Gradient Metrics to instantly display Social Network Analysis values on your link chart. Select an SNA metric and Sentinel Visualizer adjusts each node on the network to reflect its value.
Only Sentinel Visualizer makes it easy to apply the power of Social Network Analysis to your data. Use it to find hidden meaning, patterns, and trends in any data set.
Here's an MIT Technology Review article mentioning Sentinel Visualizer on Building a Picture of the Boston Marathon Bombing Suspects through Social Network Analysis.
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