C implementations of various important algorithms in graph theory for shortest paths and minimal spanning trees. Run make or make all to compile. This will produce the following executables:

• dijkstra (make dijkstra)
• prim (make prim)
• kruskal (make kruskal)
• bellmanford (make bellmanford)

Then run them the regular way, e.g., ./dijsktra or optionally with one of the provided sample graphs like so - ./prim < samplegraph1.txt

• Source
  • graph_input.c - Graph input code that is a common dependency for all the other driver files
  • dijkstra.c - Implementation of Dijkstra's single-source shortest path algorithm with a priority queue
  • prim.c - Implementation of Prim's algorithm to find minimal spanning trees
  • kruskal.c - Implementation of Kruskal's algorithm to find minimal spanning trees with the Union-Find data structure
  • bellmanford.c - Implementation of Bellman-Ford single-source shortest path algorithm with negative edges
• Sample graphs
  • samplegraph1.txt
    • Undirected weighted graph to test with Dijkstra's, Prim's, Kruskal's and Bellman-Ford
    • Illustrates that shortest paths and minimal spanning trees are not the same problem because Dijkstra's and Bellman-Ford give a different result from Prim's and Kruskal's
  • samplegraph2.txt
    • Directed graph with negative weights and a negative cycle to test with Bellman-Ford
    • Shows that Bellman-Ford can detect a negative cycle
    • Running Dijkstra's on this sample graph will break it because the graph input program loops till it gets a non-negative edge weight
    • Prim's and Kruskal's do not run on directed graphs
  • samplegraph3.txt
    • Directed graph with negative weights with no negative cycle to test with Bellman-Ford
    • Outputs shortest paths with negative weights since there is no negative cycle
    • Does not run with Dijkstra's, Prim's and Kruskal's for the reasons described above

The graphs are represented with a O(N) list of node names and an O(N^2) edge matrix. This representation made sense because in a toy application like this, I expected to be dealing with denser and smaller graphs rather than large, sparse ones.

• Graph input program: O(N^2)
• Dijkstra's algorithm: O(N^2)
  • newPriorityQueue: O(N)
  • Main loop executes O(N) times because it runs until every node is visited
    • allNodesVisited: O(N)
    • extractMinDistance: O(N)
    • Inner for loop executes O(N) times because it checks for an edge with every node
      • validNeighbour: O(1)
      • changeDistance: O(1)
    • changeDistance: O(1)
• Prim's algorithm: O(N^2)
  • This algorithm is implemented almost exactly like Dijkstra's and thus has the same time complexity
• Kruskal's algorithm: O(M*N^2)
  • newUnionFind: O(N)
  • Main loop executes O(M) times because it runs until every edge is visited
    • allEdgesVisited: O(N^2)
    • Inner loops run in O(N^2)
    • Find: O(1)
    • Union: O(N)
    • deleteEdge: O(1)
• Bellman-Ford algorithm: O(N^3)
  • Initializing distances and previous arrays: O(N)
  • Main loop executes O(N^3) times
  • The loop to check for negative cycles runs in O(N^2)

As usual, N = number of nodes, M = number of edges.