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Information flow

As explained in Advanced Algorithms and Data Structures

Flow is an assignment of real-valued amounts to network edges that respects edge capacities and conserves flow at intermediate vertices.

Information flow assigns a real-valued amount to every edge in a directed network. Each edge has a capacity, and every intermediate vertex must pass onward exactly the flow it receives. section 18.4.1

Model the problem with these elements:

  • Use a source s with only outgoing edges and a sink t with only incoming edges. section 18.4.1
  • Treat edge capacity as the maximum permitted amount, such as the volume a pipe can carry. section 18.4.1
  • Route inbound flow through intermediate vertices without increasing its total amount. section 18.4.1

Choosing a useful route

If vertex D can receive five units but can theoretically send six, its actual outbound flow cannot exceed its inbound flow. When the route from F to the sink carries only one unit, sending D’s flow toward G can increase the sink’s total flow. section 18.4.1

How Advanced Algorithms and Data Structures covers it

How deep it goes
Defines edge assignments, capacities, source and sink structure, and conservation at intermediate vertices.
Examples
Uses hydraulic pipes, vertex D’s routing choice, and network diagrams with candidate flow solutions.
What it assumes
Assumes directed weighted graphs while introducing network roles, capacities, and flow conservation.
What you can do after
Evaluate whether a proposed edge assignment obeys capacities and flow conservation while seeking greater sink inflow.
How it connects
Connects directed graphs, networks, edge capacity, source, sink, and maximum flow.

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This explainer was generated by Manning's knowledge graph from the text of Advanced Algorithms and Data Structures. The section links point to the parts of the book it draws on.