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  2. Shortest path problem - Wikipedia

    en.wikipedia.org/wiki/Shortest_path_problem

    Shortest path (A, C, E, D, F), blue, between vertices A and F in the weighted directed graph. In graph theory, the shortest path problem is the 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.

  3. Dijkstra's algorithm - Wikipedia

    en.wikipedia.org/wiki/Dijkstra's_algorithm

    Dijkstra's algorithm finds the shortest path from a given source node to every other node. [7]: 196–206 It can be used to find the shortest path to a specific destination node, by terminating the algorithm after determining the shortest path to the destination node. For example, if the nodes of the graph represent cities, and the costs of ...

  4. Betweenness centrality - Wikipedia

    en.wikipedia.org/wiki/Betweenness_centrality

    Percolation centrality is defined for a given node, at a given time, as the proportion of ‘percolated paths’ that go through that node. A ‘percolated path’ is a shortest path between a pair of nodes, where the source node is percolated (e.g., infected). The target node can be percolated or non-percolated, or in a partially percolated state.

  5. A* search algorithm - Wikipedia

    en.wikipedia.org/wiki/A*_search_algorithm

    The algorithm continues until a removed node (thus the node with the lowest f value out of all fringe nodes) is a goal node. [b] The f value of that goal is then also the cost of the shortest path, since h at the goal is zero in an admissible heuristic. The algorithm described so far only gives the length of the shortest path.

  6. Pathfinding - Wikipedia

    en.wikipedia.org/wiki/Pathfinding

    The node is marked "closed", and all nodes adjacent to it are added to the open set if they have not already been examined. This process repeats until a path to the destination has been found. Since the lowest distance nodes are examined first, the first time the destination is found, the path to it will be the shortest path. [4]

  7. Parallel all-pairs shortest path algorithm - Wikipedia

    en.wikipedia.org/wiki/Parallel_all-pairs...

    A central problem in algorithmic graph theory is the shortest path problem. Hereby, the problem of finding the shortest path between every pair of nodes is known as all-pair-shortest-paths (APSP) problem. As sequential algorithms for this problem often yield long runtimes, parallelization has shown to be beneficial in this field. In this ...

  8. Average path length - Wikipedia

    en.wikipedia.org/wiki/Average_path_length

    Average path length of the bipartite graph and the cube graph. Average path length, or average shortest path length is a concept in network topology that is defined as the average number of steps along the shortest paths for all possible pairs of network nodes. It is a measure of the efficiency of information or mass transport on a network.

  9. Shortest-path tree - Wikipedia

    en.wikipedia.org/wiki/Shortest-path_tree

    Construct the shortest-path tree using the edges between each node and its parent. The above algorithm guarantees the existence of shortest-path trees. Like minimum spanning trees, shortest-path trees in general are not unique. In graphs for which all edge weights are equal, shortest path trees coincide with breadth-first search trees.