Temporally-aware dynamic network analysis: traversing nodegoat graphsCORE Admin
During the conference 'Graphs and Networks in the fourth dimension – time and temporality as categories of connectedness', jointly organised by the Historical Network Research community and Graphs & Networks in the Humanities, we presented a new nodegoat feature: 'temporally-aware dynamic network analysis'. This new functionality extends the Scope and Chronology Statements.
The Scope functionality is used throughout nodegoat to traverse your data model and select elements to be included in a visualisation, analysis, or export. With the Scope, you can limit or expand your data selection. In a prosopographical analyses you might want to include all educational institutes related to one person, plus all the relations of these institutes, while omitting all other personal relations of a person. Follow this Guide to learn how to configure a Scope.
Chronology Statements that you make in nodegoat allow you to specify what you mean by a statement like 'circa'. Instead of using qualitative statements about vagueness, Chronology Statements provide you with a way of making quantitative statements about vagueness. Chronology Statements also allow you to make relational date statements: 'the date point is between the sending of letter X and the sending of letter Y'. Follow this Guide to learn how to store uncertain dates by using Chronology Statements and follow this Guide to learn how to store relational dates by using Chronology Statements.
The temporally-aware dynamic network analysis functionality makes the temporal options offered by the Chronology Statements available on any level of a Scope. This allows you to apply and pass temporality to time-bound connections in any of a Scope's paths. The dates from Chronology Statements can be sourced from every step in the traversal: ascendant or descendant nodes, and combinations. Selected configurations can be applied on any/all of the connections/edges: outbound or inbound directionality, and combinations.
Example: Academic Connections
With this functionality it is now possible to dynamically generate networks of people who attended the same educational institute at the same time, without specifying any dates in a filter. The temporally-aware dynamic network analysis functionality applies the initial date on every other relationship that appears on a specified path:
The obvious benefit of this approach is the scalability of this functionality, as it allows you to quickly scrutinise complex networks based on time-bound connections:
How to use temporally-aware dynamic network analysis functionality
As with all nodegoat functionalities: the way in which you can make use of this feature is dependent on the data model that you have implemented. To be able to run temporally-aware dynamic network analyses, you will need to have a model where Objects can be connected with each other via temporally attributed relationships.
For the example shown above, you would use the following Scope that follows the connection of people to institutes, and include all other people connected to this institute. Check the checkbox labeled 'T' to only include time-bound relationships ('Apply and pass temporality to all subsequent time-bound connections in the path').
Multi-modal graphs with dynamic time-bound weight can be translate to other multi-modal graphs, or be collapsed into bipartite and unimodal graphs.
When applying Network Analysis to the resulting graph, a larger temporally traversed graph may include a drifting temporal scope. The drift would depend on n-th order of generated (collapsed) edge. An algorithmically reapplied or persistent temporal scope could solve this, but requires approaches that keep efficiency in mind.