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Graphs can be represented and processed using adjacency lists or matrices, which are data structures that store node connections.

In computer science, a graph is a data structure that consists of a set of nodes (or vertices) and a set of edges that connect these nodes. There are two common ways to represent these graphs in a computer: adjacency lists and adjacency matrices.

An adjacency list is a collection of unordered lists used to represent a finite graph. Each list describes the set of neighbours of a vertex in the graph. This is the more space-efficient way to represent a graph, as it only needs to store the edges that exist. To find all the vertices adjacent to a particular vertex, you simply go to the list for that vertex. Each edge in the graph is stored once. Therefore, an adjacency list is particularly useful for sparse graphs, where the number of edges is much less than the number of vertices squared.

On the other hand, an adjacency matrix is a square matrix used to represent a finite graph. The elements of the matrix indicate whether pairs of vertices are adjacent or not in the graph. In the adjacency matrix, each row and column represents a vertex. If there is an edge between two vertices, then the corresponding cell in the matrix is 1 (or sometimes the weight of the edge), otherwise it is 0. This method is more suitable for dense graphs, where the number of edges is close to the number of vertices squared.

Processing graphs involves traversing the graph, which can be done using algorithms like Depth-First Search (DFS) or Breadth-First Search (BFS). For DFS in an adjacency list, you would start at a chosen vertex and explore as far as possible along each branch before backtracking. For BFS, you would visit all the vertices at the current depth before going to the next depth level. In an adjacency matrix, these algorithms would involve traversing the rows (or columns) of the matrix.

In conclusion, whether you choose to use an adjacency list or matrix to represent and process a graph depends on the characteristics of the graph and the operations you need to perform on it.

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