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How to perform Edmonds-Karp on a graph

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Get ready to dive into the exciting world of network flow problems! If you’re looking for a powerful algorithm that can help you calculate the maximum flow between two points in a graph, look no further than Edmonds-Karp.

This clever variation of the Ford-Fulkerson algorithm has proven to be incredibly useful for solving scheduling and routing problems, as well as finding the shortest path between two vertices.

In this article, we’ll take a deep dive into Edmonds-Karp, exploring its inner workings and sharing some tips to help you get the most out of this powerful algorithm.

What is Edmonds-Karp?

Edmonds-Karp algorithm is a variation of the Ford-Fulkerson algorithm that can help you find the maximum flow between two points in a graph. This powerful algorithm is perfect for optimizing routes, scheduling and routing problems, and even finding the shortest path between two vertices.

How does it work? Edmonds-Karp algorithm repeatedly searches for paths that have not yet been explored and adds their flow to the total, allowing you to find the most efficient flow between two points.

But that’s not all! Edmonds-Karp algorithm is not only efficient but also incredibly fast, thanks to its polynomial time complexity. This means that no matter how large your graph is, the algorithm will always find the optimal solution in a reasonable amount of time. Plus, it’s relatively easy to implement, making it a popular choice for solving network flow problems.

Applying the Edmonds-Karp Algorithm to a Graph

The Edmonds-Karp algorithm is a fantastic way to solve one of the most exciting problems in graph theory: finding the maximum flow in a flow network. Whether you’re a computer science student, a math enthusiast, or a problem-solving lover, the Edmonds-Karp algorithm is an excellent tool to have in your arsenal.

Here are the steps to apply the Edmonds-Karp algorithm to a graph:

  • Initialize the flow network: Start with a graph that represents the flow network, where each edge has a capacity and a flow of 0. Also, select a source node and a sink node.
  • Calculate the residual graph: Create a residual graph from the flow network, which represents the remaining capacity of each edge. For each edge, the residual capacity is the difference between the capacity and the current flow. If the flow is greater than the capacity, then the residual capacity is 0.
  • Find an augmenting path: Use a breadth-first search to find a path from the source to the sink in the residual graph. The path must have positive residual capacity for each edge.
  • Calculate the bottleneck capacity: Determine the bottleneck capacity of the path, which is the minimum residual capacity of all the edges in the path.
  • Update the flow: Increase the flow of each edge in the path by the bottleneck capacity, and decrease the residual capacity of each edge by the same amount.
  • Repeat steps 2-5: Continue to calculate the residual graph, find an augmenting path, calculate the bottleneck capacity, and update the flow until there are no more augmenting paths in the residual graph.
  • Compute the maximum flow: The maximum flow is the sum of the flow going out of the source node.
  • Compute the minimum cut: To find the minimum cut, use the residual graph to identify the set of nodes that can be reached from the source node. The minimum cut is the set of edges that have one endpoint in the set of nodes that can be reached from the source node, and the other endpoint in the set of nodes that cannot be reached.
  • Terminate the algorithm:The algorithm terminates when there are no more augmenting paths in the residual graph.

The time complexity of the Edmonds-Karp algorithm is O(E^2 * V), where E is the number of edges and V is the number of nodes in the flow network. However, with the use of advanced data structures such as a priority queue, the time complexity can be reduced to O(E * V^2 * log(V)).

Example of performing Edmonds-Karp on a graph

Let’s work through an example of performing the Edmonds-Karp algorithm on a graph to find the maximum flow.

Consider the following graph:

10
   /     \
s /       \ t
 /         \
A --4--> B
 \         /
c \       / d
   \     /
     15

Here, s is the source node, t is the sink node, and the edges are labeled with their capacities.

1. Initialization: Set the flow f to 0, and find an augmenting path using BFS.

Starting from node s, the algorithm looks for an edge with available capacity, and finds the edge (s, A) with a capacity of 10. This becomes the first edge in the augmenting path.

f = 0
Path = []
Queue = [s]

2. BFS: Perform BFS to find the augmenting path.

The algorithm performs a BFS search to find the shortest augmenting path from s to t. Since the edge (s, A) has available capacity, it is added to the augmenting path.

f = 0
Path = [(s, A)]
Queue = [A]

From A, the algorithm looks for an adjacent node with available capacity, and finds the edge (A, B) with a capacity of 4. This is added to the augmenting path.

f = 0
Path = [(s, A), (A, B)]
Queue = [B]

From B, the algorithm looks for an adjacent node with available capacity, and finds the edge (B, t) with a capacity of 10. This is added to the augmenting path.

f = 0
Path = [(s, A), (A, B), (B, t)]
Queue = []

The augmenting path from s to t has been found.

3. Updating flow: Update the flow along the augmenting path.

The algorithm updates the flow along the augmenting path from s to t by adding the minimum capacity of the edges in the path, which is 4.

f = 4
Path = [(s, A), (A, B), (B, t)]
Queue = []

The residual graph is updated by subtracting the flow from the capacities of the forward edges, and adding the flow to the capacities of the backward edges.

6
   /     \
s /       \ t
 /         \
A --0--> B
 \         /
c \       / d
   \     /
     15

4. Repeat: Repeat the above steps until there are no more augmenting paths.

The algorithm repeats the above steps until there are no more augmenting paths.

From s, the algorithm looks for an edge with available capacity, and finds the edge (s, A) with a capacity of 6. This becomes the first edge in the augmenting path.

f = 4
Path = []
Queue = [s]

Performing BFS, the algorithm finds the augmenting path (s, A, B, t). The minimum capacity along the path is 6, so the flow is updated accordingly.

f = 10
Path = [(s, A), (A, B), (B, t)]
Queue = []

The residual graph is updated again.

0
   /     \
s /       \ t
 /         \
A --2--> B
 \         /
c \       / d
   \     /
     15

Thereare no more augmenting paths from s to t, so the algorithm terminates.

5. Final result: The maximum flow is the sum of the flow along all the augmenting paths.

In this case, the maximum flow is 10, which is the flow along the two augmenting paths found by the algorithm.

The final flow network looks like this:

4
     /   \
s   /     \  t
   /       \
A -4-> B -6->
 |       |
  \     /
  15   10

Here, the edges are labeled with their current flow and capacity values. The dashed lines represent the edges in the original graph that have been saturated.

And that’s how you can perform the Edmonds-Karp algorithm on a graph to find the maximum flow!

Benefits of Using the Edmonds-Karp Algorithm

The Edmonds-karp algorithm offers several benefits that make it an excellent choice for solving maximum flow problems in a variety of applications. Here are some of the benefits of using the Edmonds-Karp algorithm:

  • Efficiency: The Edmonds-Karp algorithm is highly efficient and runs in O(V * E^2) time complexity.
  • Accuracy: The Edmonds-Karp algorithm is known for its accuracy in finding the maximum flow in a network. It always returns the optimal solution, ensuring that the maximum amount of flow is transported from the source to the sink.
  • Simplicity: The Edmonds-Karp algorithm is relatively easy to understand and implement. It is a modified version of the Ford-Fulkerson algorithm, which makes use of a breadth-first search to find the augmenting path. This makes it easier to implement than other algorithms that use more complex search methods.
  • Robustness: The Edmonds-Karp algorithm is robust in the sense that it can handle a wide range of network topologies and capacities. It can be used to solve problems involving multiple sources and sinks, as well as problems with complex capacities and weights on the edges.
  • Wide Applicability: The Edmonds-Karp algorithm can be used in a variety of applications, including transportation and logistics, telecommunications, and computer networking. It is also useful in solving other problems related to network flow, such as the maximum cut problem.

Common Pitfalls When Implementing Edmonds-Karp

While the Edmonds-Karp algorithm is a powerful tool for solving maximum flow problems, there are several common pitfalls that can arise when implementing it. Here are some of the most common pitfalls to watch out for:

  • Inefficient Data Structures: The Edmonds-Karp algorithm relies heavily on data structures such as queues, which can become inefficient if implemented incorrectly. It is important to choose the right data structures and optimize them for the specific problem at hand to ensure the algorithm runs efficiently.
  • Incorrect Implementation of the BFS: The Edmonds-Karp algorithm uses a breadth-first search to find the shortest augmenting path. It is crucial to implement the BFS correctly, as errors in the search can lead to incorrect results or even an infinite loop.
  • Inadequate Initialization: The initialization of variables and data structures is critical to the success of the algorithm. Failure to initialize variables properly can lead to unpredictable results and errors.
  • Insufficient Handling of Multiple Paths: In some cases, there may be multiple augmenting paths of the same length from the source to the sink. It is important to handle these paths correctly to ensure that the maximum flow is computed accurately.
  • Lack of Error Handling:The Edmonds-Karp algorithm can encounter various errors, such as overflow or underflow of variables or invalid input. It is important to implement proper error handling to avoid unexpected results and ensure the algorithm runs smoothly.
  • Failure to Terminate: In rare cases, the Edmonds-Karp algorithm may fail to terminate due to the existence of negative cycles in the network. It is important to detect and handle such cases to prevent the algorithm from running indefinitely.

Tips for Optimizing Performance with Edmonds-Karp

When using Edmonds-Karp, there are several tips that can be used to optimize performance.

  • Use adjacency lists: The algorithm relies heavily on graph traversals, and using adjacency lists instead of adjacency matrices can significantly reduce the time complexity.
  • Implement path compression: During the BFS stage, path compression can be used to reduce the number of nodes that need to be visited, thus improving the performance.
  • Use a priority queue: When implementing the BFS stage, using a priority queue to store the nodes can improve the performance. This is because it ensures that nodes are visited in the order of their distance from the source node.
  • Use efficient data structures: Depending on the implementation, using efficient data structures such as hash tables or balanced trees can improve the performance.
  • Avoid unnecessary computations: In some cases, unnecessary computations can be avoided by early termination of the algorithm or by taking advantage of special cases.
  • Test with real-world data: Finally, it is important to test the algorithm with real-world data to ensure that it performs well in practical scenarios.

Conclusion

Edmonds-Karp is a powerful algorithm for finding the maximum flow between two vertices in a graph. It can be applied quickly and efficiently, and can handle large graphs with a small amount of memory.

By understanding how the algorithm works and following best practices for implementation, developers can optimize its performance and ensure that issues are quickly addressed.

The Edmonds-Karp algorithm is a great tool for developers who need to find the maximum flow between two vertices in a graph. It is easy to implement and can be used to solve a variety of problems.

Additionally, it is a reliable algorithm that can be used to quickly and accurately find the maximum flow between two vertices in a graph.

Anand Das

Anand Das

Anand is Co-founder and CTO of Bito. He leads technical strategy and engineering, and is our biggest user! Formerly, Anand was CTO of Eyeota, a data company acquired by Dun & Bradstreet. He is co-founder of PubMatic, where he led the building of an ad exchange system that handles over 1 Trillion bids per day.

Amar Goel

Amar Goel

Amar is the Co-founder and CEO of Bito. With a background in software engineering and economics, Amar is a serial entrepreneur and has founded multiple companies including the publicly traded PubMatic and Komli Media.

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