Get Bito’s latest Global Developer Report on AI Use in Software Development! Download Now
Get Bito’s latest report on AI Use in Software Development! Download Now

## Best AI Code Assistant

### Trusted by

100K+ Devs Worldwide

## Adjacency List Graph Java: Java Explained

An adjacency list graph is a popular form of representing a graph in Java. An adjacency list graph uses a collection of linked lists to represent which vertices (or nodes) of the graph are adjacent to each other. In this article we will look at the advantages, disadvantages and ways to work with adjacency list graphs in Java.

## What is an Adjacency List Graph?

An adjacency list graph is a type of graph that uses a linked list to store a list of vertices (or nodes) that are adjacent to a given vertex. The nodes are connected to each other using an edge, which can be either directed or undirected. The traditional adjacency list uses an array to store the linked lists, with one list for each vertex. The structure of an adjacency list looks like this:

Vertex 1: [Vertex 2, Vertex 3]

Vertex 2: [Vertex 1, Vertex 4]

Vertex 3: [Vertex 1, Vertex 4]

Vertex 4: [Vertex 2, Vertex 3]

This example shows an undirected graph with four vertices. The vertices are shown in square brackets and the edges are shown by their relation to the vertices – vertex 1 is linked to vertex 2 and 3 and vice versa. This example is a simple example of an adjacency list, it can be much more complicated depending on the type of graph.

Adjacency list graphs are useful for representing relationships between objects, such as in social networks or transportation networks. They are also used in algorithms such as Dijkstra’s algorithm, which is used to find the shortest path between two vertices in a graph.

Adjacency list graphs also have the advantage of being able to represent more complex relationships between vertices than an adjacency matrix. For example, an adjacency list can represent a directed graph, which is a graph with edges that have a direction associated with them. This is not possible with an adjacency matrix, as it can only represent undirected graphs. Additionally, adjacency lists can represent weighted graphs, which are graphs with edges that have a numerical value associated with them. This is also not possible with an adjacency matrix.

Another disadvantage of adjacency list graphs is that they are not as efficient when it comes to searching for a particular vertex or edge. This is because the linked list structure requires the algorithm to traverse the entire list in order to find the desired element. This can be especially problematic when dealing with large graphs as the time required to search for a particular element can become quite long.

## Creating an Adjacency List Graph in Java

Creating an adjacency list graph in Java requires a basic knowledge of Java data structures including classes and interfaces such as lists and sets. Typically, you will want to start by creating a class or interface for your graph. Your class or interface will be used to store your graph’s vertices and edges. You should also create a class or interface for each vertex that you want your graph to have which will define the properties of the vertex.

Once your classes and interfaces are created, you must create a linked list for each vertex. Initially, this might be empty as you add nodes and edges one at a time. You can use the following code to create a basic linked list for your vertex:

``List<Vertex> list = new LinkedList<>();``

You can then use this list to add and remove nodes from the graph as well as store information about the edges between those nodes.

## Working with Adjacency Lists in Java

Once your adjacency list is created, you can use it to store graph data. There are several ways to do this. One way is to create functions that allow you to add, remove and edit nodes and edges within your graph. For example, you could create functions that take two vertex objects as parameters and create an edge between them like this:

``public void addEdge(Vertex v1, Vertex v2){    v1.getList().add(v2);     // add v2 as an edge for v1     v2.getList().add(v1);     // add v1 as an edge for v2   }``

This code snippet adds two directed edges from vertex 1 to vertex 2 and vice versa.

## Traversing an Adjacency List Graph in Java

Once your adjacency list is created, you can traverse it in various ways depending on the type of task that you wish to perform. There are two main ways for traversing graphs: depth-first search and breadth-first search. Depth-first search works by starting at a root node, exploring as far as possible along each branch before backtracking. Breadth-first search starts at the root node and explores all adjacent nodes before moving on to the next level.

To implement a depth-first search on your adjacency list, use a stack. Start by pushing the root node onto your stack, then pop it off and traverse it’s linked list of adjacent nodes. Push each node onto your stack until you reach a node that is already on your stack, indicating that you have reached the end of this branch of your graph. From here you can backtrack by popping off the stack until you reach a node with other adjacent nodes that you have yet to explore.

## Optimizing Performance with an Adjacency List Graph in Java

There are several ways to optimize the performance of your adjacency list graph in Java. One way is to use a hashmap instead of an array for organizing your nodes and edges. A hashmap allows you to access any element directly instead of having to traverse elements linearly which can significantly reduce run time in some cases. Additionally, when adding or deleting edges from your graph, you can use lazy deletion or lazy insertion for increasing performance by only updating the necessary parts of your graph.

## Conclusion

In this article we’ve explored what an adjacency list graph is, looked at its advantages and disadvantages, ways of working with it in Java, traversing your adjacency list graph, and optimizing performance. Adjacency list graphs are a great way of storing data in Java and with the correct implementation they can be quicker and more efficient than using an adjacency matrix.

#### 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.

## How Index Works in SQL: Enhancing Query Performance

Get Bito for IDE of your choice