### Key Concepts

We often want to find the shortest path from A to B, where A and B are expressed as nodes, and linked by edges in some graph structure. This simple problem description is non-trivial because of certain properties of the problem and increasingly relaxed assumptions about the problem.

1. Are the edges positive, negative, weighted?
2. What do we mean by shortest path? Distance travelled? Time taken?
3. Are the routes that are blocked? etc.
4. We want to do this all as efficiently as possible hence we compare different implementations in the runtime analysis.

### Model Preliminaries

• A graph is an abstract representation that models connections between objects. These can be social networks, websites, cities, or even currency exchange paths. We need efficient graph algorithms to compute on graphs, in particular, we are interested in computing shortest paths.
• Formally, a graph $G=(V, E)$ where $V$ are the nodes and $E$ represents the edges.
• We assume adjacency list representation, where each node has a list of its neighboring graphs and the weight or distance of the edge from itself to its neighbors.

### Algorithm 1: Breadth-first Search + Shortest Path Tree

#### Graph properties:

Edges are directed and have equal weight of 1. The shortest path is thus the number of edges traversed.

#### Intuition:

This algorithm processes nodes with increasing distance away from the starting node. There are two separate steps for this algorithm, 1. Discovery and 2. Processing. New nodes are discovered by processing old nodes. The shortest path can be obtained by backtracking from the target node back to the source.

Example where we are provided with the Graph Structure (from an adjacency list). Which can be visualised from a dot file. The start node $s$ explores its neighboring nodes, $c$, $d$, $a$. Nodes which are further away from the source have an increasingly lighter color shade.

#### Implementation

The algorithm can be implemented with the following:

• Queue: Discovered nodes are processed in First-in-First-Out order
• HashMap1: To store node distances from the source. Nodes only need to be discovered once.
• HashMap2: To store the preceeding node which led to the current node.

Initialise the distance hashmap by setting all distances to infinity.

To run the Breadth-first search procedure on any input node, we first enqueue the input node, and set its distance to 0. Next, we process the (source) node and find (target) nodes that are linked to it. If the target nodes have not been explored before, i.e. distance is inf, then we enqueue the target node and (1) update its distance by adding the current node’s distance +1. and (2) keep track of the node that preceeded it.

To find the shortest path from node S to node e, we backtrack from e’s previous nodes back to S. Note that we are appending to the list in reverse order, hence we reverse the final result to find the shortest path from S to e.

#### Runtime analysis

The runtime of breadth-first-search is $O(V+E)$. Of which there is a cute analysis where if $(V)\leq(E)$, then $(V+E)\leq(E+E)=2.E$. Conversely, if $(V)\geq(E)$, then $(V+E)\leq(V+V)=2.V$. Thus $O(V+E)=O(max(V, E))$.

### Algorithm 2: Djikstra’s

#### Graph properties:

Edges are directed and have positive lengths. The shortest path is thus the shortest edge distance traversed (not the number of edges).

#### Intuition:

In the course of node exploration, we might discover new node, $u$, that offer a shorter path to a previously discovered node, $v$. When this happens, we should update the distance of the previously discovered node to the new shortest path, and the previously discovered node of $v$ to $u$. The algorithm follows a greedy strategy by repeatedly processing the node with minimum shortest path estimate.

#### Implementation

To implement the algorithm efficiently, we need mechanisms to:

1. Update the order of processing nodes, after their updated distance. The next node to process should have minimum distance value from the starting node. We implement this with a priority queue from the heap data structure and initialise the queue to contain all nodes.

Note: Heapify after adding each node is an $O(n)$ operation. Hence we use the work-around recommended by python docs which does not disrupt the original heap but continuously adds to the heap.

1. Update the distance of target nodes $v$, which has outgoing edges from the current node $u$. (i.e relax edge distance from $u$ to $v$)

The full implementation follows the spirit of breadth-first search exploration. We pop nodes from the priority queue, find edges that link the source node to target nodes, and check if we can ‘relax edges’ i.e. have any updated distances for the target nodes. If the distance has changed, we update the priority queue. The while loop iterates exactly $|V|$ times, as we add nodes to the priority queue exactly once(although we may update its values). In each iteration, we remove a node from the priority queue until all nodes have been processed.

#### Runtime Analysis

The running time analysis depends on the data structure used to implement the priority queue

1. Create the data structure
2. Find and extract the next node to process - we need to do this operation $V$ times (once for each node).
3. Change the priority of the nodes - we need to do this operation $E$ times (once for each edge).
• Array: $O(V + V^2 + E) = O(V^2)$
• Create takes $O(V)$.
• Find and extract takes $O(V)$, which would be $O(V^2)$ for all operations.
• Change takes $O(1)$ for a single operation, which would be $O(E)$ for all operations.

• Binary Heap (our implementation): $O(V + VlogV + ElogV)$ = $O((V+E)logV)$
• Create takes $O(V)$.
• Find and extract takes $O(logV)$, which would be $O(VlogV)$ for all operations.
• Change takes $O(logV)$ time, which would be $O(ElogV)$ for all operations. In our implementation, the size of the binary heap has a maximum size of $|E|$, which is at most $|V|^2$ if the graph is fully connected. $O(log(V^2))$ = $O(2log(V))$ = $O(logV)$

If $|E|$ is small, then the binary heap implementation is much better than the array. If $|E|$ is close to $|V|^2$, then the array implementation is better because the binary heap runtime becomes $O(V^2logV)$

### Algorithm 3: Bellman-Ford

#### Graph Properties

Graph edges can have directed negative weights. The shortest path thus takes into account negative distances traversed.

#### Intuition

The Bellman-Ford iterates over the Graph $|V|-1$ times, and relaxes all edges $E$ in the graph at each iteration. Unlike Djikstra’s algorithm which assumes that all edge weights are positive, and can selectively relax edge weights based on the sequence of node explorations (implemented by the priority queue), the Bellman-Ford can not assume the correct sequence of node and edge exploration and hence iterates through everything with brute-force.

#### Implementation

As a result of the brute-force procedure, the implementation is considerably simpler than Djikstra’s. The algorithm is guaranteed to converge after $|V|-1$ iterations to the right distances if no negative cycles exist. Hence, a convergence check shows whether or not there are negative cycles in the graph.

#### Runtime Analysis

The Bellman-Ford runs in $O(V + VE) = O(VE)$ time:

• Initialisation takes $O(V)$.
• The nested for-loop takes $O(VE)$. Iterating over all the edges over the graph takes $O(E)$, and we do this $O(V-1)$ times.

#### To come..

• A-star
• Bidirectional Djikstra
• Floyd warshall