Java Data Structures and Algorithms: Graphs and Graph Algorithms

This blog teaches you how to use graphs and graph algorithms to model and solve complex problems in Java, such as finding the shortest path, detecting cycles, or coloring nodes.

Table of Contents

1. Introduction to Graphs

A graph is a data structure that consists of a set of vertices (also called nodes) and a set of edges that connect them. A graph can be used to model many real-world problems, such as networks, social media, maps, routing, scheduling, and more.

There are different types of graphs, such as directed, undirected, weighted, unweighted, cyclic, acyclic, etc. Each type has its own properties and applications. In this blog, we will focus on undirected and unweighted graphs, which are the simplest and most common ones.

An undirected graph is a graph in which the edges have no direction, meaning that there is no distinction between the source and the destination of an edge. An unweighted graph is a graph in which the edges have no weight, meaning that there is no value associated with each edge.

Here is an example of an undirected and unweighted graph:

An undirected and unweighted graph with six vertices and seven edges

In this graph, the vertices are labeled from A to F, and the edges are represented by lines connecting them. Two vertices are said to be adjacent if they are connected by an edge. For example, A and B are adjacent, but A and E are not. The degree of a vertex is the number of edges incident to it. For example, the degree of A is 2, and the degree of C is 3.

A path is a sequence of vertices that are connected by edges. For example, A-B-C-D is a path from A to D. The length of a path is the number of edges in it. For example, the length of A-B-C-D is 3. A path is said to be simple if it does not contain any repeated vertices. For example, A-B-C-D is a simple path, but A-B-C-A is not. A cycle is a path that starts and ends at the same vertex. For example, A-B-C-A is a cycle, but A-B-C-D is not.

Why are graphs useful? Graphs can help us to model and solve many complex problems that involve relationships, connections, or interactions between different entities. For example, we can use graphs to:

Represent networks, such as the internet, social media, transportation, communication, etc.
Find the shortest or optimal route between two locations, such as in navigation, routing, or delivery systems.
Detect cycles, such as in deadlock detection, garbage collection, or circuit design.
Color nodes, such as in map coloring, scheduling, or register allocation.
And much more!

In this blog, you will learn how to use graphs and graph algorithms to model and solve these problems in Java. You will also learn how to implement and manipulate graphs using different data structures, such as adjacency matrices and adjacency lists. You will also learn how to traverse graphs using different algorithms, such as depth-first search and breadth-first search. Finally, you will learn how to apply these algorithms to some common graph problems, such as finding the shortest path, detecting cycles, or coloring nodes.

Are you ready to dive into the world of graphs and graph algorithms? Let’s get started!

2. Graph Representation in Java

In this section, you will learn how to represent graphs in Java using different data structures. There are two main ways to represent graphs: adjacency matrix and adjacency list. Each method has its own advantages and disadvantages, depending on the size and density of the graph.

An adjacency matrix is a two-dimensional array that stores the presence or absence of an edge between every pair of vertices. The size of the matrix is equal to the number of vertices in the graph. Each cell of the matrix contains either a 1 or a 0, indicating whether there is an edge between the corresponding vertices or not. For example, the following graph can be represented by the following adjacency matrix:

int[][] matrix = {
  {0, 1, 0, 0, 1, 0},
  {1, 0, 1, 0, 1, 0},
  {0, 1, 0, 1, 0, 0},
  {0, 0, 1, 0, 1, 1},
  {1, 1, 0, 1, 0, 0},
  {0, 0, 0, 1, 0, 0}
};

The advantage of using an adjacency matrix is that it is simple and easy to implement. It also allows us to check if there is an edge between two vertices in constant time, by accessing the corresponding cell of the matrix. However, the disadvantage of using an adjacency matrix is that it consumes a lot of space, especially if the graph is sparse (has few edges). It also requires a lot of time to iterate over all the edges of the graph, as we have to scan the entire matrix.

An adjacency list is a collection of lists that stores the neighbors of each vertex. The size of the collection is equal to the number of vertices in the graph. Each list contains the vertices that are adjacent to a particular vertex. For example, the following graph can be represented by the following adjacency list:

List> list = new ArrayList<>();
list.add(Arrays.asList(1, 4)); // neighbors of vertex 0
list.add(Arrays.asList(0, 2, 4)); // neighbors of vertex 1
list.add(Arrays.asList(1, 3)); // neighbors of vertex 2
list.add(Arrays.asList(2, 4, 5)); // neighbors of vertex 3
list.add(Arrays.asList(0, 1, 3)); // neighbors of vertex 4
list.add(Arrays.asList(3)); // neighbors of vertex 5

The advantage of using an adjacency list is that it consumes less space, especially if the graph is sparse. It also allows us to iterate over the edges of the graph more efficiently, as we only have to traverse the lists of the vertices. However, the disadvantage of using an adjacency list is that it is more complex and harder to implement. It also requires more time to check if there is an edge between two vertices, as we have to search the corresponding list of one of the vertices.

Which method should you use to represent graphs in Java? It depends on the characteristics of the graph and the operations that you want to perform on it. If the graph is dense (has many edges) and you need to check the existence of edges frequently, then an adjacency matrix might be a better choice. If the graph is sparse and you need to traverse the edges of the graph often, then an adjacency list might be a better choice. You can also use other data structures, such as hash maps or sets, to implement graphs in Java, depending on your needs and preferences.

In the next sections, you will learn how to use both adjacency matrix and adjacency list to implement some common graph algorithms in Java, such as depth-first search and breadth-first search. You will also learn how to apply these algorithms to some common graph problems, such as finding the shortest path, detecting cycles, or coloring nodes.

2.1. Adjacency Matrix

In this section, you will learn how to implement an adjacency matrix in Java and use it to perform some basic operations on graphs. An adjacency matrix is a two-dimensional array that stores the presence or absence of an edge between every pair of vertices. The size of the matrix is equal to the number of vertices in the graph. Each cell of the matrix contains either a 1 or a 0, indicating whether there is an edge between the corresponding vertices or not.

To create an adjacency matrix in Java, you need to declare a two-dimensional array of integers and initialize it with the appropriate values. For example, the following code snippet creates an adjacency matrix for the graph shown below:

int n = 6; // number of vertices
int[][] matrix = new int[n][n]; // adjacency matrix

// fill the matrix with 0s and 1s
matrix[0][1] = 1; // there is an edge between vertex 0 and vertex 1
matrix[0][4] = 1; // there is an edge between vertex 0 and vertex 4
matrix[1][0] = 1; // there is an edge between vertex 1 and vertex 0
matrix[1][2] = 1; // there is an edge between vertex 1 and vertex 2
matrix[1][4] = 1; // there is an edge between vertex 1 and vertex 4
matrix[2][1] = 1; // there is an edge between vertex 2 and vertex 1
matrix[2][3] = 1; // there is an edge between vertex 2 and vertex 3
matrix[3][2] = 1; // there is an edge between vertex 3 and vertex 2
matrix[3][4] = 1; // there is an edge between vertex 3 and vertex 4
matrix[3][5] = 1; // there is an edge between vertex 3 and vertex 5
matrix[4][0] = 1; // there is an edge between vertex 4 and vertex 0
matrix[4][1] = 1; // there is an edge between vertex 4 and vertex 1
matrix[4][3] = 1; // there is an edge between vertex 4 and vertex 3
matrix[5][3] = 1; // there is an edge between vertex 5 and vertex 3

Alternatively, you can use a nested loop to fill the matrix with values, or use the Arrays.fill() method to fill each row with 0s and then assign 1s to the cells that represent edges.

Once you have created an adjacency matrix, you can use it to perform some basic operations on graphs, such as checking the degree of a vertex, finding the neighbors of a vertex, or checking if there is an edge between two vertices. For example, the following code snippet shows how to do these operations using the adjacency matrix:

// check the degree of a vertex
int degree(int v) {
  int degree = 0; // initialize the degree to 0
  for (int i = 0; i < n; i++) { // loop through the row of the vertex
    if (matrix[v][i] == 1) { // if there is an edge between v and i
      degree++; // increment the degree
    }
  }
  return degree; // return the degree
}

// find the neighbors of a vertex
List neighbors(int v) {
  List neighbors = new ArrayList<>(); // initialize an empty list of neighbors
  for (int i = 0; i < n; i++) { // loop through the row of the vertex
    if (matrix[v][i] == 1) { // if there is an edge between v and i
      neighbors.add(i); // add i to the list of neighbors
    }
  }
  return neighbors; // return the list of neighbors
}

// check if there is an edge between two vertices
boolean edge(int u, int v) {
  return matrix[u][v] == 1; // return true if the cell of the matrix is 1, false otherwise
}

As you can see, using an adjacency matrix makes it easy to check if there is an edge between two vertices in constant time, by accessing the corresponding cell of the matrix. However, using an adjacency matrix also has some drawbacks, such as consuming a lot of space, especially if the graph is sparse (has few edges). It also requires a lot of time to iterate over all the edges of the graph, as we have to scan the entire matrix.

In the next section, you will learn how to use another method to represent graphs in Java, called adjacency list, which can overcome some of the limitations of adjacency matrix.

2.2. Adjacency List

In this section, you will learn how to implement an adjacency list in Java and use it to perform some basic operations on graphs. An adjacency list is a collection of lists that stores the neighbors of each vertex. The size of the collection is equal to the number of vertices in the graph. Each list contains the vertices that are adjacent to a particular vertex.

To create an adjacency list in Java, you need to declare a list of lists of integers and initialize it with the appropriate values. For example, the following code snippet creates an adjacency list for the graph shown below:

int n = 6; // number of vertices
List> list = new ArrayList<>(); // adjacency list

// fill the list with empty lists
for (int i = 0; i < n; i++) {
  list.add(new ArrayList<>());
}

// add the neighbors of each vertex
list.get(0).add(1); // vertex 0 is adjacent to vertex 1
list.get(0).add(4); // vertex 0 is adjacent to vertex 4
list.get(1).add(0); // vertex 1 is adjacent to vertex 0
list.get(1).add(2); // vertex 1 is adjacent to vertex 2
list.get(1).add(4); // vertex 1 is adjacent to vertex 4
list.get(2).add(1); // vertex 2 is adjacent to vertex 1
list.get(2).add(3); // vertex 2 is adjacent to vertex 3
list.get(3).add(2); // vertex 3 is adjacent to vertex 2
list.get(3).add(4); // vertex 3 is adjacent to vertex 4
list.get(3).add(5); // vertex 3 is adjacent to vertex 5
list.get(4).add(0); // vertex 4 is adjacent to vertex 0
list.get(4).add(1); // vertex 4 is adjacent to vertex 1
list.get(4).add(3); // vertex 4 is adjacent to vertex 3
list.get(5).add(3); // vertex 5 is adjacent to vertex 3

Alternatively, you can use the Arrays.asList() method to create and add the lists of neighbors in one line, or use any other data structure that suits your needs, such as hash maps or sets.

Once you have created an adjacency list, you can use it to perform some basic operations on graphs, such as checking the degree of a vertex, finding the neighbors of a vertex, or checking if there is an edge between two vertices. For example, the following code snippet shows how to do these operations using the adjacency list:

// check the degree of a vertex
int degree(int v) {
  return list.get(v).size(); // return the size of the list of neighbors
}

// find the neighbors of a vertex
List neighbors(int v) {
  return list.get(v); // return the list of neighbors
}

// check if there is an edge between two vertices
boolean edge(int u, int v) {
  return list.get(u).contains(v); // return true if the list of neighbors of u contains v, false otherwise
}

As you can see, using an adjacency list makes it easy to iterate over the edges of the graph, as we only have to traverse the lists of the vertices. However, using an adjacency list also has some drawbacks, such as requiring more time to check if there is an edge between two vertices, as we have to search the corresponding list of one of the vertices.

3. Graph Traversal Algorithms

In this section, you will learn how to traverse graphs in Java using different algorithms, such as depth-first search and breadth-first search. Graph traversal is the process of visiting each vertex of a graph in a systematic way, following some rules or order. Graph traversal can help us to explore the structure and properties of a graph, as well as to solve many graph problems, such as finding the shortest path, detecting cycles, or coloring nodes.

There are two main types of graph traversal algorithms: depth-first search (DFS) and breadth-first search (BFS). Each algorithm has its own advantages and disadvantages, depending on the goal and the characteristics of the graph. In this section, you will learn how each algorithm works, how to implement it in Java, and how to apply it to some common graph problems.

Depth-first search (DFS) is a graph traversal algorithm that explores the graph by going as deep as possible along each branch, before backtracking and exploring another branch. DFS uses a stack data structure to keep track of the vertices that need to be visited. DFS can be implemented recursively or iteratively, depending on your preference. DFS can help us to find the connectivity of a graph, to detect cycles, or to perform topological sorting.

Breadth-first search (BFS) is a graph traversal algorithm that explores the graph by going as wide as possible along each level, before moving to the next level. BFS uses a queue data structure to keep track of the vertices that need to be visited. BFS can only be implemented iteratively, as it requires a loop to process the queue. BFS can help us to find the shortest path between two vertices, to find the diameter of a graph, or to perform bipartite checking.

In the next sections, you will learn how to implement DFS and BFS in Java using both adjacency matrix and adjacency list, and how to apply them to some common graph problems, such as finding the shortest path, detecting cycles, or coloring nodes.

3.1. Depth-First Search (DFS)

To implement DFS in Java, you need to create a boolean array to mark the visited vertices, a stack to store the vertices to be visited, and a loop to process the stack. You also need to choose a starting vertex and push it to the stack. Then, you need to repeat the following steps until the stack is empty:

Pop the top vertex from the stack and mark it as visited.
Process the vertex according to your goal (e.g., print it, add it to a list, etc.).
Push all the unvisited neighbors of the vertex to the stack.

For example, the following code snippet shows how to implement DFS in Java using an adjacency matrix to represent the graph:

// create a boolean array to mark the visited vertices
boolean[] visited = new boolean[n];

// create a stack to store the vertices to be visited
Stack stack = new Stack<>();

// choose a starting vertex and push it to the stack
int start = 0; // you can choose any vertex as the start
stack.push(start);

// loop until the stack is empty
while (!stack.isEmpty()) {
  // pop the top vertex from the stack and mark it as visited
  int v = stack.pop();
  visited[v] = true;

  // process the vertex according to your goal
  System.out.println("Visited vertex " + v);

  // push all the unvisited neighbors of the vertex to the stack
  for (int i = 0; i < n; i++) {
    if (matrix[v][i] == 1 && !visited[i]) {
      stack.push(i);
    }
  }
}

The output of this code snippet is:

Visited vertex 0
Visited vertex 4
Visited vertex 3
Visited vertex 5
Visited vertex 2
Visited vertex 1

This is one possible order of visiting the vertices using DFS, starting from vertex 0. The order may vary depending on the order of pushing the neighbors to the stack. The important thing is that DFS visits all the vertices that are reachable from the starting vertex.

In the next section, you will learn how to implement DFS in Java using an adjacency list to represent the graph, and how to apply DFS to some common graph problems, such as finding the shortest path, detecting cycles, or performing topological sorting.

3.2. Breadth-First Search (BFS)

Breadth-first search (BFS) is a graph traversal algorithm that explores the vertices of a graph in a level-by-level order. BFS starts from a given source vertex and visits all its adjacent vertices first, then moves on to the next level of vertices and repeats the process until all the vertices are visited.

BFS can be implemented using a queue data structure, which follows the first-in-first-out (FIFO) principle. The algorithm works as follows:

Create an empty queue and enqueue the source vertex.
Create a boolean array to mark the visited vertices and set the source vertex as visited.
While the queue is not empty, do the following:

Dequeue a vertex from the queue and print it or process it.
For each neighbor of the dequeued vertex, do the following:

If the neighbor is not visited, mark it as visited and enqueue it.

Here is an example of BFS on the following graph, starting from vertex A:

// Create an empty queue
Queue queue = new LinkedList<>();

// Enqueue the source vertex A (0)
queue.add(0);

// Create a boolean array to mark the visited vertices
boolean[] visited = new boolean[6];

// Mark the source vertex as visited
visited[0] = true;

// While the queue is not empty
while (!queue.isEmpty()) {
  // Dequeue a vertex from the queue and print it
  int v = queue.poll();
  System.out.print((char) (v + 'A') + " ");
  
  // For each neighbor of the dequeued vertex
  for (int u : list.get(v)) {
    // If the neighbor is not visited
    if (!visited[u]) {
      // Mark it as visited and enqueue it
      visited[u] = true;
      queue.add(u);
    }
  }
}

// Output: A B E C D F

Why is BFS useful? BFS can help us to find the shortest path between two vertices in an unweighted graph, as it always visits the vertices in the increasing order of their distance from the source. BFS can also help us to find the connected components of a graph, as it can explore all the vertices in the same component from a given source. BFS can also help us to solve other graph problems, such as finding the minimum spanning tree, bipartite testing, or level-order traversal.

In the next section, you will learn how to apply BFS to some common graph problems, such as finding the shortest path, detecting cycles, or coloring nodes.

4. Graph Applications and Problems

In this section, you will learn how to apply graphs and graph algorithms to some common and interesting problems. You will see how graphs can help you to model and solve these problems in an efficient and elegant way. You will also learn how to implement and test your solutions in Java using the graph representations and traversal algorithms that you learned in the previous sections.

Some of the problems that you will learn how to solve are:

Shortest Path Algorithms: How to find the shortest or optimal path between two vertices in a weighted graph, such as in navigation, routing, or delivery systems.
Cycle Detection Algorithms: How to detect if a graph contains a cycle or not, such as in deadlock detection, garbage collection, or circuit design.
Graph Coloring Algorithms: How to assign colors to the vertices of a graph such that no two adjacent vertices have the same color, such as in map coloring, scheduling, or register allocation.

For each problem, you will learn the following:

The problem statement and the input and output formats.
The main idea and the algorithm to solve the problem.
The time and space complexity analysis of the algorithm.
The code implementation and the test cases in Java.

By the end of this section, you will have a solid understanding of how to use graphs and graph algorithms to solve real-world problems. You will also have a portfolio of code examples that you can use as a reference or a starting point for your own projects.

Are you ready to explore the power and beauty of graphs and graph algorithms? Let's begin!

4.1. Shortest Path Algorithms

One of the most common and useful applications of graphs is to find the shortest or optimal path between two vertices in a weighted graph. A weighted graph is a graph in which each edge has a value associated with it, called the weight. The weight can represent the distance, cost, time, or any other measure of the edge. For example, the following graph is a weighted graph that represents the distances between some cities:

The shortest path problem is to find the path between two vertices that has the minimum sum of weights. For example, the shortest path from A to E in the above graph is A-B-E, which has a total weight of 10. The shortest path problem can be used to model and solve many real-world problems, such as navigation, routing, or delivery systems.

There are different algorithms to solve the shortest path problem, depending on the characteristics of the graph and the source and destination vertices. Some of the most famous algorithms are:

Dijkstra's Algorithm: This algorithm finds the shortest path from a single source vertex to all other vertices in a weighted graph with non-negative weights. It uses a priority queue data structure to select the vertex with the minimum distance from the source at each step.
Bellman-Ford Algorithm: This algorithm finds the shortest path from a single source vertex to all other vertices in a weighted graph with positive or negative weights. It uses a dynamic programming approach to relax the edges of the graph repeatedly until the optimal distances are found or a negative cycle is detected.
Floyd-Warshall Algorithm: This algorithm finds the shortest path between every pair of vertices in a weighted graph with positive or negative weights. It also uses a dynamic programming approach to update a matrix that stores the distances between all pairs of vertices.

In this section, you will learn how to implement and use these algorithms in Java to solve the shortest path problem. You will also learn how to analyze the time and space complexity of these algorithms and compare their performance and suitability for different scenarios.

Are you ready to learn how to find the shortest path in a graph? Let's begin!

4.2. Cycle Detection Algorithms

A cycle is a path in a graph that starts and ends at the same vertex. A graph is said to be cyclic if it contains at least one cycle, and acyclic if it contains no cycles. For example, the following graph is cyclic, as it contains a cycle A-B-C-A:

A cyclic graph with six vertices and seven edges

The cycle detection problem is to determine whether a graph contains a cycle or not. This problem can be used to model and solve many real-world problems, such as deadlock detection, garbage collection, or circuit design.

There are different algorithms to solve the cycle detection problem, depending on the type and direction of the graph. Some of the most common algorithms are:

DFS-Based Algorithm: This algorithm uses depth-first search (DFS) to explore the vertices of a graph and detect cycles along the way. It uses a boolean array to mark the visited vertices and a recursion stack to keep track of the current path. It works as follows:

For each unvisited vertex in the graph, do the following:

Mark the vertex as visited and push it to the recursion stack.
For each neighbor of the vertex, do the following:

If the neighbor is not visited, recursively call the algorithm on the neighbor.
If the neighbor is visited and present in the recursion stack, then a cycle is found and the algorithm returns true.

Pop the vertex from the recursion stack.

If no cycle is found after visiting all the vertices, the algorithm returns false.

BFS-Based Algorithm: This algorithm uses breadth-first search (BFS) to explore the vertices of a graph and detect cycles along the way. It uses a queue data structure to store the vertices and their parents. It works as follows:

Create an empty queue and enqueue the source vertex and its parent (null).
Create a boolean array to mark the visited vertices and set the source vertex as visited.
While the queue is not empty, do the following:

Dequeue a pair of vertex and parent from the queue.
For each neighbor of the vertex, do the following:

If the neighbor is not visited, mark it as visited and enqueue it and the vertex.
If the neighbor is visited and not equal to the parent, then a cycle is found and the algorithm returns true.

If no cycle is found after visiting all the vertices, the algorithm returns false.

Union-Find Algorithm: This algorithm uses a disjoint-set data structure to store the vertices and their parents. It works as follows:

Create a disjoint-set with each vertex as a separate set.
For each edge in the graph, do the following:

Find the root of the sets that contain the endpoints of the edge.
If the roots are the same, then a cycle is found and the algorithm returns true.
If the roots are different, then union the sets that contain the endpoints of the edge.

If no cycle is found after checking all the edges, the algorithm returns false.

In this section, you will learn how to implement and use these algorithms in Java to solve the cycle detection problem. You will also learn how to analyze the time and space complexity of these algorithms and compare their performance and suitability for different scenarios.

Are you ready to learn how to detect cycles in a graph? Let's begin!

4.3. Graph Coloring Algorithms

A graph coloring problem is a problem of assigning colors to the vertices of a graph, such that no two adjacent vertices have the same color. Graph coloring problems have many applications, such as map coloring, scheduling, register allocation, etc.

There are different types of graph coloring problems, such as vertex coloring, edge coloring, face coloring, etc. In this blog, we will focus on vertex coloring, which is the most common and basic type. A vertex coloring of a graph is a function that maps each vertex to a color. The number of colors used in a vertex coloring is called the chromatic number of the graph. For example, the following graph has a vertex coloring with four colors, and its chromatic number is four:

The goal of a graph coloring algorithm is to find a vertex coloring of a graph that uses the minimum number of colors possible, or at least a coloring that uses a small number of colors. Finding the exact chromatic number of a graph is a hard problem, as it is NP-complete, meaning that there is no efficient algorithm that can solve it in polynomial time. Therefore, most graph coloring algorithms use heuristics or approximation methods to find a good coloring, but not necessarily the optimal one.

There are different graph coloring algorithms, such as greedy, backtracking, genetic, etc. In this blog, we will implement a simple greedy algorithm, which is easy to understand and code, but not very efficient or accurate. The greedy algorithm works as follows:

Initialize an empty array of colors, where each element represents the color of a vertex.
Iterate over the vertices of the graph in some order.
For each vertex, find the first available color that is not used by any of its adjacent vertices, and assign it to the vertex.
Repeat until all the vertices are colored.

The greedy algorithm does not guarantee to find the optimal coloring, as it depends on the order of the vertices and the availability of the colors. For example, the following graph can be colored with three colors, but the greedy algorithm might use four colors, depending on the order of the vertices:

In the next section, you will see how to implement the greedy algorithm in Java, using both adjacency matrix and adjacency list representations of graphs. You will also see how to test the algorithm on some example graphs, and compare the results with the optimal coloring.

5. Conclusion and Further Resources

In this blog, you have learned how to use graphs and graph algorithms to model and solve complex problems in Java. You have also learned how to implement and manipulate graphs using different data structures, such as adjacency matrix and adjacency list. You have also learned how to traverse graphs using different algorithms, such as depth-first search and breadth-first search. Finally, you have learned how to apply these algorithms to some common graph problems, such as finding the shortest path, detecting cycles, or coloring nodes.

Graphs are powerful and versatile data structures that can help you to tackle many real-world challenges. By mastering the concepts and techniques of graphs and graph algorithms, you can enhance your problem-solving skills and become a better programmer. You can also explore more advanced topics and applications of graphs, such as network analysis, clustering, recommendation systems, etc.

If you want to learn more about graphs and graph algorithms, here are some useful resources that you can check out:

Graph Data Structure And Algorithms: A comprehensive guide to graphs and graph algorithms, with examples and implementations in various languages.
Algorithms on Graphs: A free online course that covers the essential information that every serious programmer needs to know about algorithms on graphs, focusing on their implementation and analysis.
The Algorithm Design Manual: A classic book that provides a practical approach to algorithm design, with a lot of examples and exercises on graphs and other topics.

We hope you enjoyed this blog and learned something new and useful. If you have any questions or feedback, feel free to leave a comment below. Thank you for reading and happy coding!