Graphs: Difference between revisions

From NovaOrdis Knowledge Base
Jump to navigation Jump to search
Line 1: Line 1:
=Internal=
=Internal=
* [[Data_Structures#Dynamic_Sets|Data Structures]]
* [[Data_Structures#Dynamic_Sets|Data Structures]]
* [[Tree|Trees]]


=Overview=
=Overview=

Revision as of 19:42, 1 October 2021

Internal

Overview

Graphs are fundamental structures in computer science. They apply directly to a large number of problems that involve physical networks - such as the phone network or the internet, or logical networks about parallel relationships between objects in general - the order in which to execute interdependent tasks, or the analysis of social networks.

Graphs are backed by mathematical formalism. The Graph Concepts page provides a number of terms, concepts and some mathematical tools that are useful when dealing with graphs. Graph Representation in Memory describes ways to represent graph nodes and edges in such a way that they can be manipulated by algorithms. The most common arrangements - adjacency lists and adjacency matrices - are discussed.

The most obvious problem that arises when dealing with graphs is to walk them: search the graph or find paths through graphs, or more generically, explore a graph and infer knowledge about it. The classical algorithms for graph exploration are breadth-first search (BFS) and depth-first search (DFS). Both these algorithms are very efficient, they are capable of exploring the graph in linear time of the number of vertices and edges O(n + m). They are described and discussed in the Graph Search page.

Building upon the basic graph search algorithms, we discuss several useful graph problems: computing the shortest path between two vertices using breadth-first search, clustering heuristics by finding connected components in an undirected graph with breadth-first search or finding strongly connected components in a directed graph with depth-first search and the topological sort of a directed acyclic graph with depth-first search.

Subjects