Node ranking

The node ranking module applies graph centrality algorithms to the weighted directed graph produced by the weight calculation stage. The goal is to identify the most influential process elements – those that propagate the most disturbance information through the network.

The primary algorithm used is eigenvector centrality, which assigns high scores to nodes that are connected to other high-scoring nodes. This is particularly effective for identifying root causes in process networks, because the source of a disturbance tends to influence many other elements either directly or indirectly.

Additional centrality measures available include betweenness centrality and other NetworkX-supported algorithms.

Results are written as:

• GML graph files with node importance scores and edge weights.

• CSV files listing nodes ranked from most to least important.

Note

The following is auto-generated documentation from the noderank module source: