# Graphs

C++ Reading: Chapter 13 of Goodrich
Java Reading: Chapter 14 of Goodrich

A graph $$G$$ is a collection $$V$$ of vertices with a collection $$E$$ of vertex pairs known as edges. Here collection is used instead of set to admit multiples.

## Graph Terminology

• Each edge is either directed or undirected. If undirected, then an edge $$(u, v)$$ is equal to the edge $$(v, u)$$.

• If all edges are directed then the graph is “directed.” If all edges are undirected then the graph is “undirected.” Otherwise, the graph is “mixed.”

• An undirected graph: Facebook
• A directed graph: Twitter
• A mixed graph: City streets
• Two vertices are adjacent if an edge connects them. Two edges are adjacent if they share a vertex.

• The endpoints of an edge are the two vertices that it connects. A directed edge has an origin and a destination.

• The degree of a vertex is the number of edges touching it. A directed graph has separate in-degree and out-degree.

• When two edges share the same pair of vertices, these are parallel edges.

• When an edge has the same vertex at both ends, it is a self loop.

• A simple graph has no parallel edges or self loops.

• A path is a sequence of vertices and edges that connect a pair of vertices.

• A cycle is a path that begins and ends at the same vertex.

• A subgraph $$H$$ of a graph $$G$$ is a graph whose vertex and edge sets are subsets of the vertex and edge sets of $$G$$.

• A graph is connected if there exists a path between any two vertices. If not, then the connected subgraphs are called connected components.

• A tree is a connected graph with no cycles.

• A forest is a not-necessarily-connected graph with no cycles.

• A spanning subgraph includes all of the vertices of $$G$$.

• A spanning tree is a spanning subgraph with no cycles.

Let $$n$$ be the number of nodes and $$m$$ be the number of edges.

$\sum_{v\ \textrm{in}\ G} \textit{deg}(v) = 2m$

$\sum_{v\ \textrm{in}\ G} \textit{indeg}(v) = \sum_{v\ \textrm{in}\ G} \textit{outdeg}(v) = m$

A simple graph has $$O(n^2)$$ edges.

If $$G$$ is an undirected and connected then $$m \ge n - 1$$.

If $$G$$ is an undirected tree then $$m = n - 1$$.

If $$G$$ is an undirected forest then $$m \le n - 1$$.

## Basic Graph ADT Operations

A graph ADT allows data to be stored at all vertices and all edges of a graph.

Operations on graphs

vertices()
edges()
insertVertex(x)
insertEdge(u, v, a)
eraseVertex(v)
eraseEdge(e)

Operations on vertices

incidentEdges(v)

Operations on edges

endVertices(e)
opposite(e, v)
isIncidentOn(e, v)

## Graph Representations

Operation Edge List Adjacency List Matrix
vertices $$O(n)$$ $$O(n)$$ $$O(n)$$
edges $$O(m)$$ $$O(m)$$ $$O(n^2)$$
endVertices $$O(1)$$ $$O(1)$$ $$O(1)$$
opposite $$O(1)$$ $$O(1)$$ $$O(1)$$
incidentEdges $$O(m)$$ $$O(\deg(v))$$ $$O(n)$$
isAdjacentTo $$O(m)$$ $$O(\min(\deg(v),\deg(w)))$$ $$O(1)$$
isIncidentOn $$O(1)$$ $$O(1)$$ $$O(1)$$
insertVertex $$O(1)$$ $$O(1)$$ $$O(n^2)$$
insertEdge $$O(1)$$ $$O(1)$$ $$O(1)$$
eraseVertex $$O(m)$$ $$O(\deg(v))$$ $$O(n^2)$$
eraseEdge $$O(1)$$ $$O(1)$$ $$O(1)$$

## Traversal

Traversal is $$O(n + m)$$

traverse($$s$$)
mark all nodes as unvisited
$$C$$.insert($$s$$)
while $$C$$ is not empty
$$v \gets C$$.remove()
mark $$v$$ visited
for each $$w$$ adjacent to $$v$$
if $$w$$ is not visited
$$C$$.insert($$w$$)

• Depth-first

When $$C$$ is a stack, the traversal will proceed as far as possible before backtracking.

• Test whether $$G$$ is connected.
• Compute a spanning tree of $$G$$, if $$G$$ is connected.
• Compute the connected components of $$G$$.
• Compute a path between two given vertices of $$G$$, if it exists.
• Compute a cycle in $$G$$, or reporting that $$G$$ has no cycles.

When $$C$$ is a queue, the traversal will proceed forward across a broad front.

• Determine the shortest path from $$s$$ to all other vertices.

## Weighted Graphs

Each edge has a weight. The length of a path is the sum of the edge weights along it. The distance from vertices $$u$$ and $$v$$ is the length of the shortest path from $$u$$ to $$v$$. The shortest path is no longer so simple.

• Shortest Path

Calculate $$D[u]$$, the distance from $$v$$ to $$u$$ for all $$u$$.

shortestPath($$v$$)
$$D[v]\gets 0$$
$$D[u]\gets \infty$$ for all $$u\neq v$$
Let $$Q$$ be a priority queue containing $$(D[u], u)$$ for all $$u$$
while $$Q$$ is not empty
$$u\gets Q$$.removeMin()
for each $$z$$ adjacent to u
if $$D[z]\gt D[u] + w(u, z)$$
$$D[z]\gets D[u] + w(u, z)$$
update $$(D[z], z)$$ in $$Q$$