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Two primary problems of pathfinding are (1) to find a path between two nodes in a graph; and (2) the shortest path problem—to find the optimal shortest path. Basic algorithms such as breadth-first and depth-first search address the first problem by exhausting all possibilities; starting from the given node, they iterate over all potential ...
For simple connected graphs, shortest-path trees can be used [1] to suggest a non-linear relationship between two network centrality measures, closeness and degree. By assuming that the branches of the shortest-path trees are statistically similar for any root node in one network, one may show that the size of the branches depend only on the ...
Counting the number of unlabeled free trees is a harder problem. No closed formula for the number t(n) of trees with n vertices up to graph isomorphism is known. The first few values of t(n) are 1, 1, 1, 1, 2, 3, 6, 11, 23, 47, 106, 235, 551, 1301, 3159, … (sequence A000055 in the OEIS). Otter (1948) proved the asymptotic estimate
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 ...
In order to reduce the prediction latency, Jimenez proposed in 2003 the fast-path neural predictor, where the perceptron predictor chooses its weights according to the current branch's path, rather than according to the branch's PC. Many other researchers developed this concept (A. Seznec, M. Monchiero, D. Tarjan & K. Skadron, V. Desmet, Akkary ...
The breadth-first-search algorithm is a way to explore the vertices of a graph layer by layer. It is a basic algorithm in graph theory which can be used as a part of other graph algorithms.
Branch decomposition of a grid graph, showing an e-separation.The separation, the decomposition, and the graph all have width three. In graph theory, a branch-decomposition of an undirected graph G is a hierarchical clustering of the edges of G, represented by an unrooted binary tree T with the edges of G as its leaves.
This option allows you to see how often each branch in your program was taken. Unconditional branches will not be shown, unless the -u option is given.-c (--branch-counts): Write branch frequencies as the number of branches taken, rather than the percentage of branches taken.-n (--no-output): Do not create the gcov output file.