Classes
Context
- Classes allow you to define sets of nodes with common properties, so you can easily select and manipulate those nodes according to their class membership.
- This is particularly helpful when you need to manage a large number of nodes in a network and arrange them meaningfully on the screen.
- You can group nodes into Classes based on fundamental properties or other characteristics derived from expert knowledge or background knowledge of the underlying data.
- For instance, you may wish to group nodes into Classes based on:
- Discretization type used during Data Import.
- Role of nodes in the network, e.g., Confounder vs. Non-Confounder, Latent Variable/Factor vs. Manifest.
- Meaning of the nodes in the context of your study, e.g., conditions, symptoms, treatments, outcomes, or costs.
- Origin of the nodes, e.g., survey data, sales data, financial data, sensor data, expert assessments, location data, laboratory data.
- The context within the domain under study, e.g., in a vehicle satisfaction survey, nodes representing consumer ratings could be grouped using broad concepts such as performance, safety, comfort, utility, and value.
- Temporal order of nodes, e.g.,
t0,t-1,t-2,2018,2019,2020. - Cluster membership, either assigned from prior knowledge or determined through BayesiaLab’s clustering algorithms.
- You can also create Classes ad hoc for convenience to perform tasks that would otherwise be tedious, e.g., grouping nodes according to the first letter in each node name.
- Also, note that nodes can be members of multiple Classes at the same time.