Structural Profiling

The structural profiling functionality allows a structure (ie. a diagram or DSM model) to be summarised as a set of numbers using various different metrics. This can help to understand the properties of a structure or of the nodes and edges within it. For more information about specific metrics, and a bibliography of sources, please refer to the publications described in the More information section at the bottom of this page. 

The first step is to decide what sort of profile you wish to create, ie. what aspect of the structure you want to analyse: 

1. Analyse the entire structure. Some metrics extract certain properties of the network (such as its modularity or connectedness), creating one number for the whole structure being analysed. Results are displayed as a table of numbers. The following metrics are available in this group:

    • Number of sets defined in the structure
    • Number of unconnected nodes
    • Number of edges per node
    • Number of edges
    • Number of edges that cross a set border
    • Number of nodes
    • Number of nodes (discounting duplicates due to shortcuts)
    • Relational density (non-zero fraction)
    • Singular-value modularity index (SMI)

2. Analyse nodes individually. Other metrics characterise individual nodes with respect to their context in the structure. Results are displayed as a parallel coordinate plot and may be exported to CSV format for further analysis in other programs. The basic idea is that, the nodes having high values for any or all of these metrics, may be in some sense more 'important', 'central' or 'critical' in the system being modelled. The following metrics are available in this group:

    • Clustering coefficient
    • Betweenness centrality
    • Indegree
    • Outdegree
    • Active closeness
    • Passive closeness
    • Reachability
    • Number of reachable nodes

3. Analyse edges individually. Similar to analysing nodes individually, but with a focus on edge properties in context of the structure. The following metrics are available:

    • Edge active sum
    • Edge passive sum

The following set-specific metrics are only available if a model contains node sets (eg. DSM clusters or multiple classes of node)

4. Analyse sets individually. These metrics allow analysis of each individual set defined in the structure. For instance, sets are defined by DSM clusters. Sets may, or may not, be disjoint. Analysing a set individually can consider:

  1. The content of the set. It is treated as an isolated substructure - and may be analysed using all of the Analyse entire structure metrics
  2. The set in context. It is treated as a single node, with inputs and outputs defined by the edges crossing its boundary. The context of this 'collapsed set' node may be analysed using all of the Analyse nodes individually metrics.
  3. Set-specific metrics. The following set-specific metrics are also available:

5. Analyse pair-wise interactions between sets. These metrics concern interactions between node sets (as defined above). For instance, you can create a DSM model of the people in an organisation and the network of communication links between them, then create multiple views that each cluster the data in different ways. One clustering may group the nodes according to individuals' job titles. Another view may cluster the same people according to the teams they belong to. These set definitions overlap; the structural profiling allows identification of intersections (such as: how many people of a given role work in each team?) and relations (how many people in role A talk to people in Team Y), and similar issues may also be studied. Results are displayed as a matrix where each cell represents the calculated value for a given pair of node sets. The following metrics are available in this category:

    • Number of edges connecting the pair of sets
    • Number of nodes in both sets
    • Intersection as fraction of set size
    • Relational density connecting sets.

More information

Many of the metrics are implementations of ideas collected by Kreimeyer and Lindemann (2012).

The SMI metric and its origin is discussed by Holtta-Otto and de Weck (2012).