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Decide whether a directed graph has a nonempty directed cycle; self-loops count. This problem is NL-complete. Membership guesses a returning walk of at most the vertex count. For hardness, layer a reachability instance into $N+1$ layers with wait arcs, then add only the backward edge from the target in the last layer to the source in the first. The otherwise acyclic layering has a cycle exactly when the original target is reachable. Zero-length paths must not be treated as cycles.

An edge joins two vertices of a graph; in a simple graph it is an unordered pair of distinct vertices.

A flow balance prescribes net outgoing minus incoming flow at a vertex. Positive $b_i$ is supply and negative $b_i$ is demand; zero is ordinary flow conservation. Summing all balances requires total supply to equal total demand. In a directed graph, these equations are represented by the oriented incidence matrix.

A choice of direction for each edge of an undirected graph, producing a directed graph.

The number of edges of a directed graph directed away from a given vertex. Assigning directions to undirected edges is an orientation of a graph.

Take a rooted directed graph $G$ with one root $r$ and one terminal vertex $a$, with its sole edge $r\to a$. Let $H$ have a root $s$ and two terminal vertices $b,c$, with edges $s\to b$ and $s\to c$. The relationis a bisimulation. Each root move can be matched by a root move ending in a related terminal vertex. At all related terminal pairs there are no further edges, so both matching conditions are vacuous. Yet the directed graphs have two and three vertices and cannot be isomorphic. Bisimilarity need not preserve the number of successors.

Here and in the next part, the second graph's edge relation is $F$, and its response vertices belong to $H$. These are the corrections to the printed relation and vertex slips in the bisimulation clauses.

Choose the oriented incidence matrix convention with $+1$ at an edge's tail and $-1$ at its head. Let $b_i$ be net supply, so ${\sum }_i{b}_i=0$. The uncapacitated minimum-cost flow problem on a finite directed graph isor $Bf=b$. Negative $b_i$ denotes net demand. Costs may have either sign; the problem can be infeasible or unbounded.

For vertex network dual potentials $\pi$, the optimization Lagrangian isThe infimum over $f\ge 0$ is finite exactly when every network reduced cost $r_{ij}=c_{ij}-{\pi }_i+{\pi }_j$ is nonnegative. Thus the Lagrangian dual problem and complementary slackness areA feasible flow and feasible network dual potentials satisfying these equalities have equal costs and are optimal by weak duality.

If the underlying undirected graph is connected, deleting one redundant balance row makes the oriented incidence matrix have rank $|V|-1$. A set of $|V|-1$ independent edge columns is exactly a spanning tree. Set all non-tree flows to zero, solve the tree balances, and check nonnegativity to obtain a basic feasible solution. Basic tree edges may have zero flow, which is degeneracy in linear programming. Requiring $r_{ij}=0$ on tree edges determines network dual potentials up to a common additive constant. If every non-tree network reduced cost is nonnegative, the tree flow is optimal. This is the basis of the network simplex algorithm. If the graph is disconnected, solve the balances separately in each component; each component must have total net supply zero and uses its own spanning tree.

A finite flow network is a directed graph with distinguished source $s$, sink $t$, and nonnegative finite flow network edge capacities $c_{ij}$ on its directed edges. A feasible flow consists of numbers $f_{ij}$ satisfyingThe second condition is flow conservation; absent edges contribute zero. The strength of a flow is its net outflow from the source,which equals net inflow to the sink by summing flow conservation over the other vertices. The maximum flow problem isNo assumption that a capacity-saturating flow at the source is feasible downstream is made. On a finite network a maximum exists, because the constraints define a nonempty compact set of edge flows and the objective is continuous.

The skeleton of a directed graph is the undirected graph obtained by replacing every directed edge with an undirected edge and forgetting its orientation. In causal structure learning, the PC algorithm first recovers this adjacency structure before learning orientations.