## Introduction

Breadth First Search (BFS) is a graph traversal algorithm that explores all the vertices of a graph in breadth-first order. It starts at the tree root (or some arbitrary node of a graph) and explores all the neighboring nodes at the present depth before moving on to the nodes at the next depth level. BFS is often used to find the shortest path between two nodes in an unweighted graph.

## Implementation

Here is an implementation of BFS in OCaml:

``````type 'a graph = ('a * 'a list) list

let bfs (graph: 'a graph) (start: 'a) : 'a list =
let rec explore visited queue =
match queue with
| [] -> visited
| node :: rest ->
if List.mem node visited then
explore visited rest
else
let neighbors = List.assoc node graph in
let new_nodes = List.filter (fun n -> not (List.mem n visited)) neighbors in
let updated_queue = rest @ new_nodes in
explore (visited @ [node]) updated_queue
in explore [] [start]
``````

The `graph` parameter is a list of tuples, where the first element is a node and the second element is a list of its neighboring nodes. The `start` parameter is the node where the search begins. The function returns a list of nodes visited in breadth-first order.

Here is an example of using the `bfs` function:

``````let graph =
[
("A", ["B"; "C"]);
("B", ["A"; "D"; "E"]);
("C", ["A"; "F"]);
("D", ["B"]);
("E", ["B"; "F"]);
("F", ["C"; "E"]);
]

let result = bfs graph "A" (* ["A"; "B"; "C"; "D"; "E"; "F"] *)

let () = List.iter print_endline result
``````

## Step-by-step Explanation

1. Create an empty `visited` list and a `queue` with the starting node in it.
2. While the `queue` is not empty, take the first node from the `queue`.
3. If the node has already been visited, skip it and continue to the next node in the `queue`.
4. If the node has not been visited, add it to the `visited` list.
5. Get the list of neighboring nodes for the current node from the `graph`.
6. Filter out the nodes that have already been visited.
7. Add the remaining nodes to the end of the `queue`.
8. Repeat steps 2-7 until the `queue` is empty.
9. Return the `visited` list.

## Complexity Analysis

The time complexity of BFS is `O(|V| + |E|)`, where `|V|` is the number of vertices and `|E|` is the number of edges in the graph. This is because BFS visits each vertex and edge exactly once.

The space complexity of BFS is `O(|V|)`, where `|V|` is the number of vertices in the graph. This is because BFS uses a queue to store the nodes to be visited, and the maximum size of the queue is the number of vertices at the maximum depth of the graph. In the worst case, this is all the vertices, so the space complexity is `O(|V|)`.