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  2. Node influence metric - Wikipedia

    en.wikipedia.org/wiki/Node_influence_metric

    It measures the diversity of self-avoiding walks which start from a given node. A walk on a network is a sequence of adjacent vertices; a self-avoiding walk visits (lists) each vertex at most once. The original work used simulated walks of length 60 to characterize the network of urban streets in a Brazilian city. [6]

  3. 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]

  4. Level ancestor problem - Wikipedia

    en.wikipedia.org/wiki/Level_ancestor_problem

    In graph theory and theoretical computer science, the level ancestor problem is the problem of preprocessing a given rooted tree T into a data structure that can determine the ancestor of a given node at a given distance from the root of the tree. More precisely, let T be a rooted tree with n nodes, and let v be an arbitrary node of T.

  5. Dijkstra's algorithm - Wikipedia

    en.wikipedia.org/wiki/Dijkstra's_algorithm

    Find the path of minimum total length between two given nodes P and Q. We use the fact that, if R is a node on the minimal path from P to Q, knowledge of the latter implies the knowledge of the minimal path from P to R. is a paraphrasing of Bellman's Principle of Optimality in the context of the shortest path problem.

  6. Longest path problem - Wikipedia

    en.wikipedia.org/wiki/Longest_path_problem

    In graph theory and theoretical computer science, the longest path problem is the problem of finding a simple path of maximum length in a given graph.A path is called simple if it does not have any repeated vertices; the length of a path may either be measured by its number of edges, or (in weighted graphs) by the sum of the weights of its edges.

  7. Tree alignment - Wikipedia

    en.wikipedia.org/wiki/Tree_alignment

    By assigning the sequence to the internal nodes of the evolutionary tree, we calculate the total score of each edge, and the sum of all edges' scores is the score of the evolutionary tree. The aim of tree alignment is to find an assigned sequence, which can obtain a maximum score, and get the final matching result from the evolutionary tree and ...

  8. Closeness centrality - Wikipedia

    en.wikipedia.org/wiki/Closeness_centrality

    The number next to each node is the distance from that node to the square red node as measured by the length of the shortest path. The green edges illustrate one of the two shortest paths between the red square node and the red circle node. The closeness of the red square node is therefore 5/(1+1+1+2+2) = 5/7.

  9. Newick format - Wikipedia

    en.wikipedia.org/wiki/Newick_format

    Tree: The full input Newick Format for a single tree Subtree: an internal node (and its descendants) or a leaf node Leaf: a node with no descendants Internal: a node and its one or more descendants BranchSet: a set of one or more Branches Branch: a tree edge and its descendant subtree. Name: the name of a node Length: the length of a tree edge.