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  2. Strongly connected component - Wikipedia

    en.wikipedia.org/wiki/Strongly_connected_component

    Several algorithms based on depth-first search compute strongly connected components in linear time.. Kosaraju's algorithm uses two passes of depth-first search. The first, in the original graph, is used to choose the order in which the outer loop of the second depth-first search tests vertices for having been visited already and recursively explores them if not.

  3. Table of simple cubic graphs - Wikipedia

    en.wikipedia.org/wiki/Table_of_simple_cubic_graphs

    The two edges along the cycle adjacent to any of the vertices are not written down. Let v be the vertices of the graph and describe the Hamiltonian circle along the p vertices by the edge sequence v 0 v 1, v 1 v 2, ...,v p−2 v p−1, v p−1 v 0. Halting at a vertex v i, there is one unique vertex v j at a distance d i joined by a chord with v i,

  4. Directed graph - Wikipedia

    en.wikipedia.org/wiki/Directed_graph

    A directed graph is weakly connected (or just connected [9]) if the undirected underlying graph obtained by replacing all directed edges of the graph with undirected edges is a connected graph. A directed graph is strongly connected or strong if it contains a directed path from x to y (and from y to x) for every pair of vertices (x, y).

  5. Edge contraction - Wikipedia

    en.wikipedia.org/wiki/Edge_contraction

    Let = (,) be a graph (or directed graph) containing an edge = (,) with .Let be a function that maps every vertex in {,} to itself, and otherwise, maps it to a new vertex .The contraction of results in a new graph ′ = (′, ′), where ′ = ({,}) {}, ′ = {}, and for every , ′ = ′ is incident to an edge ′ ′ if and only if, the corresponding edge, is incident to in .

  6. Mycielskian - Wikipedia

    en.wikipedia.org/wiki/Mycielskian

    Applying the Mycielskian repeatedly, starting with the one-edge graph, produces a sequence of graphs M i = μ(M i−1), sometimes called the Mycielski graphs. The first few graphs in this sequence are the graph M 2 = K 2 with two vertices connected by an edge, the cycle graph M 3 = C 5 , and the Grötzsch graph M 4 with 11 vertices and 20 edges.

  7. Clique problem - Wikipedia

    en.wikipedia.org/wiki/Clique_problem

    The clique problem arises in the following real-world setting. Consider a social network, where the graph's vertices represent people, and the graph's edges represent mutual acquaintance. Then a clique represents a subset of people who all know each other, and algorithms for finding cliques can be used to discover these groups of mutual friends.

  8. Distance matrix - Wikipedia

    en.wikipedia.org/wiki/Distance_matrix

    In general, a distance matrix is a weighted adjacency matrix of some graph. In a network, a directed graph with weights assigned to the arcs, the distance between two nodes of the network can be defined as the minimum of the sums of the weights on the shortest paths joining the two nodes (where the number of steps in the path is bounded). [2]

  9. Boxicity - Wikipedia

    en.wikipedia.org/wiki/Boxicity

    The boxicity of a graph is the minimum dimension in which a given graph can be represented as an intersection graph of axis-parallel boxes. That is, there must exist a one-to-one correspondence between the vertices of the graph and a set of boxes, such that two boxes intersect if and only if there is an edge connecting the corresponding vertices.