COMP2521

Data Structures & Algorithms

Week 5.3

Graph Traversal

 

Author: Hayden Smith 2021

In this lecture

Why?

  • Graphs aren't very useful if all we're doing is storing things in them. We need to effectively search and traverse them to gather information

What?

  • Traverse and searching a graph
  • Breadth-first-search
  • Depth-first-search

 

Why do we use graphs?

Allow us to ask these kinds of questions:

  • Can we remove an edge and keep the graph connected?
  • Is a vertex reachable starting from somewhere else?
  • What is the quickest way to get from one node to another?
  • Is there a tree that links all vertices?

Why do we use graphs?

There are 3 topics we will focus on relating to traversal. All rely on very similar algorithms and processes, they're just used to different extents.

Traverse for Item Searching

Using an algorithm to move between nodes to look for a value

Traverse for Path Finding

Using an algorithm to look for a value, but keep track of how to get there

Traverse Fully

Like path finding, except we're just exploring the entire graph, not looking for specific value

Graph Traversal (/Search)

Graph Traversal is the systematic exploration of a graph via the edges. The focus is often between exploring paths between a starting vertex and a finish vertex.

 

  • Solutions can be iterative or recursive
  • Sometimes need to store the path we explore as we explore it, and sometimes store a list of nodes we've visited

 

Graph Traversal

There are two primary methods we'll be using to traverse a graph:

  • Depth-first search (go deep)
    • Iterative & recursive solutions
  • Breadth-first search (go wide)
    • Iterative solutions

Graph Traversal

Both approaches ignore some edges by remembering previously visited vertices (this also prevents cycles)

 

Breadth First Search

Traversing with BFS is about start at a node, and expanding out equally from there. You visit all the neighbours of the current vertex, then you visit all the neighbours of those neighbours. Etc.

 

BFS - Example Path Find

BFS - Algorithm

A very tricky algorithm to describe recursively. Typically we do it iteratively

findPathBFS(G,src,dest):
  visited[] // store previously visited node
  for all vertices v∈G:
     visited[v]=-1
  found = false
  visited[src] = src
  enqueue src into queue Q
  while not found ∧ Q is not empty:
    dequeue v from Q
    if v = dest:
       found = true
    else:
      for each (v,w) ∈ edges(G) with visited[w] = -1:
         visited[w] = v
         enqueue w into Q
  end while
  if found:
     display path in dest..src order

BFS - Cost Analysis

For an adjacency list representation:

Each vertex visited at most once. Cost => O(V)

Visit all edges incident on visited vertices. Cost => O(E)

 

BFS: O(V + E) (adjacency list)

 

Depth First Search

Traversing with DFS is about going as deep as possible until you reach a dead end, and then unwinding back through nodes until there is another branch to take.

DFS - Algorithm

An iterative DFS is identical to an iterative BFS, except we use a stack instead of a queue.

findPathDFS(G,src,dest):
  visited[] // store previously visited node
  for all vertices v ∈ G:
     visited[v] = -1
  found = false
  visited[src] = src
  push src into stack S
  while not found ∧ Q is not empty:
    pop stack from S
    if v = dest:
       found = true
    else:
      for each (v,w) ∈ edges(G) with visited[w] = -1:
         visited[w] = v
         push w into stack S
  end while
  if found:
     display path in dest..src order

DFS - Recursive

Recursive solutions are a bit more elegant.

 

Full Traversal

Path Checking

visited = {}

depthFirst(G,v):
  visited = visited ∪ {v}
  for all (v,w) ∈ edges(G):
    if w ∉ visited:
      depthFirst(G,w)

visited = {}

hasPath(G,src,dest):
  return dfsPathCheck(G,src,dest)

dfsPathCheck(G,v,dest):
  visited = visited ∪ {v}
  for all (v,w) ∈ edges(G):
    if w = dest:   // found edge to dest
      return true
    else if w ∉ visited:
      if dfsPathCheck(G,w,dest):
        return true  // found path via w to dest
  return false  // no path from v to dest

Path Finding

visited[] // store previously visited node
          
findPath(G,src,dest):
  for all vertices v ∈ G:
    visited[v] = -1
  visited[src] = src  // starting node of the path
  if dfsPathCheck(G,src,dest):
    // show path in dest..src order
    v = dest
    while v ≠ src:
       print v"-"
       v = visited[v]
    print src

dfsPathCheck(G,v,dest):
  for all (v,w) ∈ edges(G):
    if visited[w] = -1:
       visited[w] = v
       if w = dest:  // found edge from v to dest
          return true
       else if dfsPathCheck(G,w,dest):
          return true  // found path via w to dest
  return false  // no path from v to dest

DFS - Recursive Path Find

DFS - Cost Analysis

For an adjacency list representation:

Each vertex visited at most once. Cost => O(V)

Visit all edges incident on visited vertices. Cost => O(E)

 

BFS: O(V + E) (adjacency list)

 

DFS - Other

DFS does not guarantee optimal solution

 

Example below clearly does not find shortest path

Full Traversal

A full traversal of a tree is essentially using BFS or DFS to find a spanning tree

 

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COMP2521 21T2 - 5.3 - Graph Traversal

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