Step 1: Understand what BFS (Breadth-First Search) is. It is an algorithm used to explore nodes and edges of a graph level by level.
Step 2: Identify what 'space complexity' means. It refers to the amount of memory space required by an algorithm to run as a function of the size of the input.
Step 3: In BFS, we use a queue to keep track of the nodes that need to be explored. This queue stores the vertices (nodes) of the graph.
Step 4: The maximum number of vertices that can be stored in the queue at any time is equal to the number of vertices in the graph, denoted as V.
Step 5: Therefore, the space required for the queue in BFS is proportional to the number of vertices, which is O(V).
Step 6: Conclude that the space complexity of BFS in a graph is O(V).
