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.
- 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
- Graph implementation using STL for competitive programming | Set 2 (Weighted graph)
- Applications of Hashing
- Applications of Breadth First Traversal
- Applications of Depth First Search
- Applications of Minimum Spanning Tree Problem
- Karger’s algorithm for Minimum Cut | Set 2 (Analysis and Applications)
- Difference between fundamental data types and derived data types
- Sum of dependencies in a graph
- Transpose graph
- BFS for Disconnected Graph
- Hypercube Graph
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