A graph is a non-linear data structure, which consists of vertices(or nodes) connected by edges(or arcs) where edges may be directed or undirected.
- In Computer science graphs are used to represent the flow of computation.
- Google maps uses graphs for building transportation systems, where intersection of two(or more) roads are considered to be a vertex and the road connecting two vertices is considered to be an edge, thus their navigation system is based on the algorithm to calculate the shortest path between two vertices.
- In Facebook, users are considered to be the vertices and if they are friends then there is an edge running between them. Facebook’s Friend suggestion algorithm uses graph theory. Facebook is an example of undirected graph.
- In World Wide Web, web pages are considered to be the vertices. There is an edge from a page u to other page v if there is a link of page v on page u. This is an example of Directed graph. It was the basic idea behind Google Page Ranking Algorithm.
- In Operating System, we come across the Resource Allocation Graph where each process and resources are considered to be vertices. Edges are drawn from resources to the allocated process, or from requesting process to the requested resource. If this leads to any formation of a cycle then a deadlock will occur.
Thus the development of algorithms to handle graphs is of major interest in the field of computer science.
- Applications of linked list data structure
- Graph Types and Applications
- Graph Coloring | Set 1 (Introduction and Applications)
- Static Data Structure vs Dynamic Data Structure
- Difference between data type and data structure
- Convert the undirected graph into directed graph such that there is no path of length greater than 1
- Maximum number of edges that N-vertex graph can have such that graph is Triangle free | Mantel's Theorem
- Detect cycle in the graph using degrees of nodes of graph
- Graph implementation using STL for competitive programming | Set 2 (Weighted graph)
- GRE Algebra | Applications
- Applications of Hashing
- Applications of Depth First Search
- Deploying Node Applications
- Applications of Breadth First Traversal
- Karger’s algorithm for Minimum Cut | Set 2 (Analysis and Applications)
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