Graph distance

graph search algorithm

algorithm used for pathfinding and graph traversal

algorithm for finding single-source shortest paths in graphs, allowing some edge weights to be negative

problem of finding a path between two vertices (or nodes) in a graph such that the sum of the weights of its constituent edges is minimized

algorithm for finding all-pairs shortest paths in graphs, allowing some edge weights to be negative

in graph theory, the minimum number of edges in a path connecting two vertices

algorithm to find shortest paths between all pairs of vertices in a sparse, edge-weighted (possibly negatively), directed graph; uses the Bellman–Ford algorithm to remove negative weights and Dijkstra’s algorithm on the rest

the problem of finding a simple path of maximum length in a given graph

square matrix (two-dimensional array) containing the distances, taken pairwise, between the elements of a set. Depending upon the application involved, the distance being used to define this matrix may or may not be a metric

In graph theory and network analysis, indicators of centrality assign numbers or rankings to nodes within a graph corresponding to their network position. Applications include identifying the most influential person(s) in a social network, key infrastructure nodes in the Internet or urban networks, super-spreaders of disease, and brain networks. Centrality concepts were first developed in social network analysis, and many of the terms used to measure centrality reflect their sociological origin. Over time, the concept has expanded substantially, leading to the development of hundreds of distin

network measure

problem in graph theory

longest distance between two vertices in a graph