Social network analysis is a way to find important nodes in a network and understand how the network interacts.

The term ‘social’ implies interactions among humans, but social network analysis can help us understand interactions between anything – from devices on an IT network to transactions between bank accounts.

Let’s take a look at the algorithms available.

### Centrality Measures

##### Degree Centrality

The degree centrality measure finds nodes with the highest number of links to other nodes in the network. Nodes with a high degree centrality have the best connections to those around them – they might be influential, or just strategically well-placed.

##### Betweenness Centrality

Nodes with a high betweenness centrality score are the ones that most frequently act as ‘bridges’ between other nodes. They form the shortest pathways of communication within the network.

Usually this would indicate important gatekeepers of information between groups.

##### Closeness Centrality

This is the measure that helps you find the nodes that are closest to the other nodes in a network, based on their ability to reach them.

To calculate this, the algorithm finds the shortest path between each node, then assigns each node a score based on the sum of all the paths.

Nodes with a high closeness value have a lower distance to all other nodes. They’d be efficient broadcasters of information.

##### PageRank

PageRank identifies important nodes by assigning each a score based upon its number of incoming links (its ‘indegree’). These links are weighted depending on the relative score of its originating node.

##### EigenCentrality

Very similar to PageRank, Eigenvector centrality is a measure of influence that takes into account the number of links each node has and the number of links their connections have, and so on throughout the network.

### Other Social Network Analysis measures

##### kCores

This can be a particularly revealing way to drill down into a graph. It works by assigning each node a ‘k’ number, defined by its degree. Nodes are then grouped by their K value and filtered out in turn.

As the low k-value nodes are removed, only clusters of increasingly tight-knit nodes remain. This can help to identify cells or gangs operating semi-autonomously within a wider community.

##### Distance / shortest path

These calculations help your users understand ways to travel through (or ‘traverse’) a network.

The distance function measures how many hops apart two nodes are in a network. Shortest path highlights the route that passes through the lowest number of nodes. Hops can also be weighted, meaning you can calculate actual distances, as well as the number of hops.