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A tree topology, or star-bus topology, is a hybrid network topology in which star networks are interconnected via bus networks. [ 1 ] [ 2 ] Tree networks are hierarchical, and each node can have an arbitrary number of child nodes.
A fat tree A 2-level fat tree with 8-port switches. The fat tree network is a universal network for provably efficient communication. [1] It was invented by Charles E. Leiserson of the MIT in 1985. [1] k-ary n-trees, the type of fat-trees commonly used in most high-performance networks, were initially formalized in 1997. [2]
Although some methods produce unrooted networks that can be interpreted as undirected versions of rooted networks, which do represent a phylogeny. Rooted phylogenetic network Let X be a set of taxa. A rooted phylogenetic network N on X is a rooted directed acyclic graph where the set of leaves is bijectively labeled by the taxa in X.
A hypertree network is a network topology that shares some traits with the binary tree network. [1] It is a variation of the fat tree architecture. [2]A hypertree of degree k depth d may be visualized as a 3-dimensional object whose front view is the top-down complete k-ary tree of depth d and the side view is the bottom-up complete binary tree of depth d.
Segment tree; Self-balancing binary search tree; Semantic resolution tree; Sentinel node; Sentinel value; Sibling node; Simplex tree; Split (phylogenetics) SPQR tree; Stern–Brocot tree; Suffix tree; Suffix tree clustering
Layout of a grid low-voltage network. A grid network is a computer network consisting of a number of computer systems connected in a grid topology. In a regular grid topology, each node in the network is connected with two neighbors along one or more dimensions. If the network is one-dimensional, and the chain of nodes is connected to form a ...
SplitsTree is a popular freeware program for inferring phylogenetic trees, phylogenetic networks, or, more generally, splits graphs, from various types of data such as a sequence alignment, a distance matrix or a set of trees.
The study of complex networks is a young and active area of scientific research [1] [2] (since 2000) inspired largely by empirical findings of real-world networks such as computer networks, biological networks, technological networks, brain networks, [3] [4] climate networks and social networks.