Social Network Analysis

Measures to understand how people, objects, and events interact

Social network analysis

Introducing social network analysis

Social network analysis is a way to understand how networks behave, and uncover the most important nodes within them.

Powerful social network visualization algorithms cut through noisy social network data to reveal parts of the network that deserve most attention.

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 some of the measures and algorithms available.

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.

Social network analysis - degree centrality

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.

Social network analysis -betweenness centrality

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.

Social network analysis - - closeness centrality

PageRank Centrality

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.

See PageRank centrality in action

Social network analysis - PageRank centrality

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.

Distance shortest path

White paper: Visualizing social networks

Get a detailed introduction to the topic of social network analysis and visualization.

Download the White Paper

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