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  2. List of graph theory topics - Wikipedia

    en.wikipedia.org/wiki/List_of_graph_theory_topics

    FKT algorithm; Flood fill; Graph exploration algorithm; Matching (graph theory) Max flow min cut theorem; Maximum-cardinality search; Shortest path. Dijkstra's algorithm; Bellman–Ford algorithm; A* algorithm; Floyd–Warshall algorithm; Topological sorting. Pre-topological order

  3. Tarjan's strongly connected components algorithm - Wikipedia

    en.wikipedia.org/wiki/Tarjan's_strongly_connected...

    Tarjan's strongly connected components algorithm is an algorithm in graph theory for finding the strongly connected components (SCCs) of a directed graph. It runs in linear time , matching the time bound for alternative methods including Kosaraju's algorithm and the path-based strong component algorithm .

  4. Weisfeiler Leman graph isomorphism test - Wikipedia

    en.wikipedia.org/wiki/Weisfeiler_Leman_graph...

    In graph theory, the Weisfeiler Leman graph isomorphism test is a heuristic test for the existence of an isomorphism between two graphs G and H. [1] It is a generalization of the color refinement algorithm and has been first described by Weisfeiler and Leman in 1968. [ 2 ]

  5. Graph theory - Wikipedia

    en.wikipedia.org/wiki/Graph_theory

    The unification of two argument graphs is defined as the most general graph (or the computation thereof) that is consistent with (i.e. contains all of the information in) the inputs, if such a graph exists; efficient unification algorithms are known.

  6. Category:Graph algorithms - Wikipedia

    en.wikipedia.org/wiki/Category:Graph_algorithms

    Graph algorithms solve problems related to graph theory. Subcategories. This category has the following 3 subcategories, out of 3 total. ...

  7. Bron–Kerbosch algorithm - Wikipedia

    en.wikipedia.org/wiki/Bron–Kerbosch_algorithm

    The basic form of the Bron–Kerbosch algorithm is a recursive backtracking algorithm that searches for all maximal cliques in a given graph G.More generally, given three disjoint sets of vertices R, P, and X, it finds the maximal cliques that include all of the vertices in R, some of the vertices in P, and none of the vertices in X.

  8. Shortest path problem - Wikipedia

    en.wikipedia.org/wiki/Shortest_path_problem

    There are a great number of algorithms that exploit this property and are therefore able to compute the shortest path a lot quicker than would be possible on general graphs. All of these algorithms work in two phases. In the first phase, the graph is preprocessed without knowing the source or target node. The second phase is the query phase.

  9. Graph edit distance - Wikipedia

    en.wikipedia.org/wiki/Graph_edit_distance

    Exact algorithms for computing the graph edit distance between a pair of graphs typically transform the problem into one of finding the minimum cost edit path between the two graphs. The computation of the optimal edit path is cast as a pathfinding search or shortest path problem, often implemented as an A* search algorithm.