Graphs

• Collection of nodes/vertices and connecting edges

• Can be (un)directed, (uni|bi)directional, weighted, (un)connected, (a)cyclic.

• Usually represented as a matrix, can also be constructed as an ADT.

• Path-finding (Traversal) Techniques:

  • Depth First Search (Pre-order)
  • Breadth First Search (Post-order)
  • Dijkstra’s Algorithm

DFS Pseudocode

current vertex = start vertex

While current vertex exists
  Add it to "visited" list
  Add to "revisit" stack
  If current vertex has edges to vertices not in "visited" list
    current vertex = unvisited vertex
  Else
    current vertex = pop from revisit stack

DFS = visited list

BFS Pseudocode

current vertex = start vertex

While current vertex exists
  Add it to "visited" list
  Add to "revisit" queue
  If current vertex has edges to vertices not in "visited" list
    current vertex = unvisited vertex
  Else
    current vertex = dequeue from revisit queue

BFS = visited list

Yes, apparently the only difference is that DFS uses a stack while BFS uses a queue!

Dijkstra’s Algorithm

Compute shortest distance between any two vertices. Requires graph be weighted.

A "distances" dict/map that contains distances from start vertex to rest of the vertices.

Do a BFS from start vertex
  Sum weight to current vertex from start
  If it is less than that in "distances", update "distances" with new path/weight