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Directed acyclic graph

As explained in Graph Neural Networks in Action

A directed acyclic graph is a directed graph containing no cycles.

A directed acyclic graph (DAG) is a directed graph with no cycles: following its arrows can never return to the starting node. It represents one-way relationships. section 1.2.2

Use DAGs when relationships must flow without looping:

  • Model causal structures in which A can cause B, but B cannot simultaneously cause A. section 1.2.2
  • Represent workflow processes and dependency chains whose steps proceed in one direction. section 1.2.2
  • Distinguish a DAG from an undirected acyclic graph: DAG edges are directed, while an acyclic graph may be undirected. section 1.2.2

Following a one-way path

In the illustrated DAG, directed edges flow from A toward F. Unlike the cyclic example, no sequence of arrows leads back to its starting node.

How Graph Neural Networks in Action covers it

How deep it goes
Defines directed edges and cycle exclusion, then connects them to causal, workflow, and dependency modeling without implementation detail.
Examples
Illustrates a DAG with arrows flowing from A to F and contrasts it with a cyclic path and an undirected acyclic graph.
What you can do after
Evaluate whether a directed graph permits return to a starting node and identify suitable causal or dependency uses.
How it connects
Distinguishes directed graphs, acyclic graphs, cyclic graphs, causal analysis, workflow processes, and dependency chains.
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This explainer was generated by Manning's knowledge graph from the text of Graph Neural Networks in Action. The section links point to the parts of the book it draws on.