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  2. 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.

  3. Nodal analysis - Wikipedia

    en.wikipedia.org/wiki/Nodal_analysis

    The current between two nodes is equal to the voltage of the node where the current exits minus the voltage of the node where the current enters the node, both divided by the resistance between the two nodes. If there are voltage sources between two unknown voltages, join the two nodes as a supernode. The currents of the two nodes are combined ...

  4. Dijkstra's algorithm - Wikipedia

    en.wikipedia.org/wiki/Dijkstra's_algorithm

    During execution, the distance of a node N is the length of the shortest path discovered so far between the starting node and N. [18] From the unvisited set, select the current node to be the one with the smallest (finite) distance; initially, this is the starting node (distance zero).

  5. Shortest path problem - Wikipedia

    en.wikipedia.org/wiki/Shortest_path_problem

    Use a shortest path algorithm (e.g., Dijkstra's algorithm, Bellman-Ford algorithm) to find the shortest path from the source node to the sink node in the residual graph. Augment the Flow: Find the minimum capacity along the shortest path. Increase the flow on the edges of the shortest path by this minimum capacity.

  6. 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.

  7. Katz centrality - Wikipedia

    en.wikipedia.org/wiki/Katz_centrality

    A simple social network: the nodes represent people or actors and the edges between nodes represent some relationship between actors. Katz centrality computes the relative influence of a node within a network by measuring the number of the immediate neighbors (first degree nodes) and also all other nodes in the network that connect to the node under consideration through these immediate neighbors.

  8. 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]

  9. 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.