Flow models & representations

These chapters apply the same map equation to richer ways of turning your data into flow. That flow carries memory, spans layers, tracks time, follows node attributes, or crosses two node types. The first three build on state nodes; if you have not met them yet, read State nodes and higher-order flow first.

• Memory and state networks: flow that depends on where the walker came from.

• Multilayer networks: a node living in several layers (relationship types) at once; the home of the inter-layer relax rate.

• Temporal networks: multilayer networks whose layers are time windows.

• Networks with metadata: folding node attributes into the objective.

• Bipartite networks: two node types, and how declaring the boundary changes the flow model.