DAGs can model many different kinds of information. For example, a spreadsheet can be modeled as a DAG, with a vertex for each cell and an edge whenever the formula in one cell uses the value from another; a topological ordering of this DAG can be used to update all cell values when the spreadsheet is changed. Similarly, topological orderings of DAGs can be used to order the compilation operations in a makefile. The program evaluation and review technique (PERT) uses DAGs to model the milestones and activities of large human projects, and schedule these projects to use as little total time as possible. Combinational logic blocks
in electronic circuit design, and the operations in dataflow programming languages, involve acyclic networks
of processing elements. DAGs can also represent collections of events and their influence on each other, either in a probabilistic structure such as a Bayesian network or as a record of historical data such as family trees or the version histories of distributed revision control systems. DAGs can also be used as a compact representation of sequence data, such as the directed acyclic word graph representation of a collection of strings, or the binary decision diagram representation of sequences of binary choices. More abstractly, the reachability relation in a DAG forms a partial order, and any finite partial order may be represented by a DAG using reachability.
Important polynomial time computational problems on DAGs include topological sorting (computing a topological ordering), construction of the transitive closure and transitive reduction (the largest and smallest DAGs with the same reachability relation, respectively) of sets, and the closure problem, in which the goal is to find a minimum-weight subset of vertices with no edges connecting them to the rest of the graph. Transforming a directed graph with cycles into a DAG by deleting as few vertices or edges as possible (the feedback vertex set and feedback edge set problem, respectively) is an NP-hard problem, but any directed graph can be made into a DAG (its condensation) by contracting each strongly connected component into a single supervertex. The problems of finding shortest paths and longest paths can be solved on DAGs in linear time, in contrast to arbitrary graphs for which shortest path algorithms are slower and longest path problems are NP-hard.
The corresponding concept for undirected graphs is a forest, an undirected graph without cycles. Choosing an orientation for a forest produces a special kind of directed acyclic graph called a polytree. However, there are many other kinds of directed acyclic graph that are not formed by orienting the edges of an undirected acyclic graph. Moreover, every undirected graph has an acyclic orientation, an assignment of a direction for its edges that makes it into a directed acyclic graph. To emphasize that DAGs are not the same thing as directed versions of undirected acyclic graphs, some authors call them acyclic directed graphs or acyclic digraphs.