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.
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.
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.
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 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.
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.
White paper: Visualizing social networks
Get a detailed introduction to the topic of social network analysis and visualization.