A matching in a Bipartite Graph is a set of the edges chosen in such a way that no two edges share an endpoint. A maximum matching is a matching of maximum size (maximum number of edges). In a maximum matching, if any edge is added to it, it is no longer a matching. There can be more than one maximum matchings for a given Bipartite Graph.

Why do we care?
There are many real world problems that can be formed as Bipartite Matching. For example, consider the following problem:
There are M job applicants and N jobs. Each applicant has a subset of jobs that he/she is interested in. Each job opening can only accept one applicant and a job applicant can be appointed for only one job. Find an assignment of jobs to applicants in such that as many applicants as possible get jobs.

We strongly recommend to read the following post first.

Ford-Fulkerson Algorithm for Maximum Flow Problem

Maximum Bipartite Matching and Max Flow Problem
Maximum Bipartite Matching (MBP) problem can be solved by converting it into a flow network (See this video to know how did we arrive this conclusion). Following are the steps.

1) Build a Flow Network
There must be a source and sink in a flow network. So we add a source and add edges from source to all applicants. Similarly, add edges from all jobs to sink. The capacity of every edge is marked as 1 unit.

2) Find the maximum flow.
We use Ford-Fulkerson algorithm to find the maximum flow in the flow network built in step 1. The maximum flow is actually the MBP we are looking for.

A simple way to implement this is to create a matrix that represents adjacency matrix representation of a directed graph with M+N+2 vertices. Call the fordFulkerson() for the matrix. This implementation requires O((M+N)*(M+N)) extra space.

Extra space can be be reduced and code can be simplified using the fact that the graph is bipartite and capacity of every edge is either 0 or 1. The idea is to use DFS traversal to find a job for an applicant (similar to augmenting path in Ford-Fulkerson). We call bpm() for every applicant, bpm() is the DFS based function that tries all possibilities to assign a job to the applicant.

In bpm(), we one by one try all jobs that an applicant ‘u’ is interested in until we find a job, or all jobs are tried without luck. For every job we try, we do following.
If a job is not assigned to anybody, we simply assign it to the applicant and return true. If a job is assigned to somebody else say x, then we recursively check whether x can be assigned some other job. To make sure that x doesn’t get the same job again, we mark the job ‘v’ as seen before we make recursive call for x. If x can get other job, we change the applicant for job ‘v’ and return true. We use an array maxR[0..N-1] that stores the applicants assigned to different jobs.

If bmp() returns true, then it means that there is an augmenting path in flow network and 1 unit of flow is added to the result in maxBPM()

Python Programming:

# Python program to find maximal Bipartite matching.
 
class Graph:
    def __init__(self,graph):
        self.graph = graph # residual graph
        self.ppl = len(graph)
        self.jobs = len(graph[0])
 
    # A DFS based recursive function that returns true if a
    # matching for vertex u is possible
    def bpm(self, u, matchR, seen):
 
        # Try every job one by one
        for v in range(self.jobs):
 
            # If applicant u is interested in job v and v is
            # not seen
            if self.graph[u][v] and seen[v] == False:
                seen[v] = True # Mark v as visited
 
                '''If job 'v' is not assigned to an applicant OR
                previously assigned applicant for job v (which is matchR[v]) 
                has an alternate job available. 
                Since v is marked as visited in the above line, matchR[v] 
                in the following recursive call will not get job 'v' again'''
                if matchR[v] == -1 or self.bpm(matchR[v], matchR, seen):
                    matchR[v] = u
                    return True
        return False
 
    # Returns maximum number of matching 
    def maxBPM(self):
        '''An array to keep track of the applicants assigned to
        jobs. The value of matchR[i] is the applicant number
        assigned to job i, the value -1 indicates nobody is
        assigned.'''
        matchR = [-1] * self.jobs
        result = 0 # Count of jobs assigned to applicants
        for i in range(self.ppl):
            # Mark all jobs as not seen for next applicant.
            seen = [False] * self.jobs
            # Find if the applicant 'u' can get a job
            if self.bpm(i, matchR, seen):
                result += 1
        return result
 
 
bpGraph =[[0, 1, 1, 0, 0, 0],
        [1, 0, 0, 1, 0, 0],
        [0, 0, 1, 0, 0, 0],
        [0, 0, 1, 1, 0, 0],
        [0, 0, 0, 0, 0, 0],
        [0, 0, 0, 0, 0, 1]]
 
g = Graph(bpGraph)
 
print ("Maximum number of applicants that can get job is %d " % g.maxBPM())
 
#This code is contributed by Neelam Yadav

Maximum number of applicants that can get job is 5