This course contains the basics of Data Structures

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Pragnya Meter Exam

**Introduction****Graph**

In computer science, a **graph **is an abstract data structure that is meant to implement the graph concept from mathematics. A graph data structure consists mainly of a finite (and possibly mutable) set of ordered pairs, called **edges **or **arcs**, of certain entities called **nodes **or **vertices**. As in mathematics, an edge (*x*,*y*) is said to **point **or **go from ***x***to ***y*. The nodes may be part of the graph structure, or may be external entities represented by integer indices or references. A graph data structure may also associate to each edge some **edge value**, such as a symbolic label or a numeric attribute (cost, capacity, length, etc.).

**A labeled graph of 6 vertices and 7 edges.**

**Operations**

The basic operations provided by a graph data structure *G *usually include

- adjacent(
*G*,*x*,*y*): tests whether there is an edge from node*x*to node*y*. - neighbors(
*G*,*x*): lists all nodes*y*such that there is an edge from*x*to*y*. - add(
*G*,*x*,*y*): adds to*G*the edge from*x*to*y*, if it is not there. - delete(
*G*,*x*,*y*): removes the edge from*x*to*y*, if it is there. - get_node_value(
*G*,*x*): returns the value associated with the node*x*. - set_node_value(
*G*,*x*,*a*): sets the value associated with the node*x*to*a*. Structures that associate values to the edges usually provide also - get_edge_value(
*G*,*x*,*y*): returns the value associated to the edge (*x*,*y*). - set_edge_value(
*G*,*x*,*y*,*v*): sets the value associated to the edge (*x*,*y*) to*v*.

**Representations**

Different data structures for the representation of graphs are used in practice, e.g.:

- An adjacency list is implemented as an array of lists, with one list of destination nodes for each**Adjacency list**

source node.- A variant of the adjacency list that allows for the description of the edges at the cost of additional**Incidence list**

edges.- A two-dimensional Boolean matrix, in which the rows and columns represent source and destination vertices and entries in the matrix indicate whether an edge exists between the vertices associated with that row and column.**Adjacency matrix**- A two-dimensional Boolean matrix, in which the rows represent the vertices and columns represent the edges. The array entries indicate if both are related, i.e. incident. Adjacency lists are preferred for sparse graphs; otherwise, an adjacency matrix is a good choice. For graphs with some regularity in the placement of edges, a symbolic graph is a possible choice of representation.**Incidence matrix**

**Algorithms**

Graph algorithms are a significant field of interest within computer science. Typical higher-level operations associated with graphs are: finding a path between two nodes, like depth-first search and breadth-first search and finding the shortest path from one node to another, like Dijkstra's algorithm. A solution to finding the shortest path from each node to every other node also exists in the form of the Floyd-Warshall algorithm. A directed graph can be seen as a flow network, where each edge has a capacity and each edge receives a flow. The Ford-Fulkerson algorithm is used to find out the maximum flow from a source to a sink in a graph.