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## Digraphs and DAGs

When we attach significance to the order in which the two vertices are specified in each edge of a graph, we have an entirely different combinatorial object known as a digraph, or directed graph. Screenshot shows a sample digraph. In a digraph, the notation s-t describes an edge that goes from s to t but provides no information about whether or not there is an edge from t to s. There are four different ways in which two vertices might be related in a digraph: no edge; an edge s-t from s to t; an edge t-s from t to s; or two edges s-t and t-s, which indicate connections in both directions. The one-way restriction is natural in many apps, easy to enforce in our implementations, and seems innocuous; but it implies added combinatorial structure that has profound implications for our algorithms and makes working with digraphs quite different from working with undirected graphs. Processing digraphs is akin to traveling around in a city where all the streets are one-way, with the directions not necessarily assigned in any uniform pattern. We can imagine that getting from one point to another in such a situation could be a challenge indeed.

##### Screenshot A directed graph (digraph)

A digraph is defined by a list of nodes and edges (bottom), with the order that we list the nodes when specifying an edge implying that the edge is directed from the first node to the second. When drawing a digraph, we use arrows to depict directed edges (top).

We interpret edge directions in digraphs in many ways. For example, in a telephone-call graph, we might consider an edge to be directed from the caller to the person receiving the call. In a transaction graph, we might have a similar relationship where we interpret an edge as representing cash, goods, or information flowing from one entity to another. We find a modern situation that fits this classic model on the Internet, with vertices representing Web pages and edges the links between the pages. In , we examine other examples, many of which model situations that are more abstract. One common situation is for the edge direction to reflect a precedence relationship. For example, a digraph might model a manufacturing line: Vertices correspond to jobs to be done, and an edge exists from vertex s to vertex t if the job corresponding to vertex s must be done before the job corresponding to vertex t. Another way to model the same situation is to use a PERT chart: Edges represent jobs and vertices implicitly specify the precedence relationships (at each vertex, all incoming jobs must complete before any outgoing jobs can begin). How do we decide when to perform each of the jobs so that none of the precedence relationships are violated? This is known as a scheduling problem. It makes no sense if the digraph has a cycle so, in such situations, we are working with directed acyclic graphs (DAGs). We shall consider basic properties of DAGs and algorithms for this simple scheduling problem, which is known as topological sorting, in s 19.5 through 19.7. In practice, scheduling problems generally involve weights on the vertices or edges that model the time or cost of each job. We consider such problems in s 21 and 22. The number of possible digraphs is truly huge. Each of the V2 possible directed edges (including self-loops) could be present or not, so the total number of different digraphs is 2V2. As illustrated in Screenshot, this number grows very quickly, even by comparison with the number of different undirected graphs and even when V is small. As with undirected graphs, there is a much smaller number of classes of digraphs that are isomorphic to each other (the vertices of one can be relabeled to make it identical to the other), but we cannot take advantage of this reduction because we do not know an efficient algorithm for digraph isomorphism.

##### Screenshot Graph enumeration

While the number of different undirected graphs with V vertices is huge, even when V is small, the number of different digraphs with V vertices is much larger. For undirected graphs, the number is given by the formula 2V(V+1)/2; for digraphs, the formula is 2V2.

Certainly, any program will have to process only a tiny fraction of the possible digraphs; indeed, the numbers are so large that we can be certain that virtually all digraphs will not be among those processed by any given program. Generally, it is difficult to characterize the digraphs that we might encounter in practice, so we design our algorithms such that they can handle any possible digraph as input. On the one hand, this situation is not new to us (for example, virtually none of the 1000! permutations of 1000 elements have ever been processed by any sorting program). On the other hand, it is perhaps unsettling to know that, for example, even if all the electrons in the universe could run supercomputers capable of processing 1010 graphs per second for the estimated lifetime of the universe, those supercomputers would see far fewer than 10-100 percent of the 10-vertex digraphs (see Exercise 19.9). This brief digression on graph enumeration perhaps underscores several points that we cover whenever we consider the analysis of algorithms and indicates their particular relevance to the study of digraphs. Is it important to design our algorithms to perform well in the worst case, when we are so unlikely to see any particular worst-case digraph? Is it useful to choose algorithms on the basis of average-case analysis, or is that a mathematical fiction? If our intent is to have implementations that perform efficiently on digraphs that we see in practice, we are immediately faced with the problem of characterizing those digraphs. Mathematical models that can convincingly describe the digraphs that we might expect in apps are even more difficult to develop than are models for undirected graphs. In this chapter, we revisit, in the context of digraphs, a subset of the basic graph-processing problems that we considered in , and we examine several problems that are specific to digraphs. In particular, we look at DFS and several of its apps, including cycle detection (to determine whether a digraph is a DAG); topological sort (to solve, for example, the scheduling problem for DAGs that was just described); and computation of the transitive closure and the strong components (which have to do with the basic problem of determining whether or not there is a directed path between two given vertices). As in other graph-processing domains, these algorithms range from the trivial to the ingenious; they are both informed by and give us insight into the complex combinatorial structure of digraphs.

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