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Multiple edge

As explained in Optimization Algorithms: AI techniques for design, planning, and control problems

A parallel edge is one of multiple edges connecting the same pair of vertices in a multigraph.

A multiple edge, also called a parallel edge, is one of several edges connecting the same pair of vertices in a multigraph. Parallel roads, capacities, or social relationships can model distinct connections between identical endpoints. section 3.1

Routing algorithms must preserve edge weights when exploring parallel edges. Choosing the wrong edge can produce a longer route even when the endpoints are identical. section 3.5

A practical normalization strategy for weighted routing graphs is:

  • Compare parallel edges connecting the same vertices. section 3.5
  • Retain the edge with the lowest weight, such as the shortest road length. section 3.5
  • Discard the other parallel edges before applying the adapted shortest-path search. section 3.5

Two routes through the same connection

Suppose vertices 0 and 1 have two parallel edges, and both connect onward to vertex 2. Selecting the upper edge can yield a route length of 7, while selecting the lower, shorter edge yields 3. section 3.5

Figure 3.38 Parallel edges may be problematic because finding the shortest path depends on which parallel edge is selected during graph exploration.

Visualizes why shortest-path results depend on which parallel edge graph exploration selects. section 3.5

How Optimization Algorithms: AI techniques for design, planning, and control problems covers it

How deep it goes
Defines parallel edges, demonstrates their shortest-path effect, and prescribes retaining the lowest-weight edge.
Examples
Uses parallel roads, a NetworkX multigraph, and routes from vertex 0 through vertex 1 to vertex 2.
What it assumes
Assumes vertices, edges, multigraphs, weights, and shortest-path search.
What you can do after
Diagnose/debug with Dijkstra’s algorithm by selecting the lowest-weight edge among parallel connections.
How it connects
Connects multigraphs, weighted graphs, shortest paths, Dijkstra’s algorithm, and self-loops.

This explainer was generated by Manning's knowledge graph from the text of Optimization Algorithms: AI techniques for design, planning, and control problems. The section links point to the parts of the book it draws on.