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The principle of maximum caliber (MaxCal) or maximum path entropy principle, suggested by E. T. Jaynes, [1] can be considered as a generalization of the principle of maximum entropy. It postulates that the most unbiased probability distribution of paths is the one that maximizes their Shannon entropy. This entropy of paths is sometimes called ...
Maximal entropy random walk (MERW) is a popular type of biased random walk on a graph, in which transition probabilities are chosen accordingly to the principle of maximum entropy, which says that the probability distribution which best represents the current state of knowledge is the one with largest entropy.
Any random graph model (at a fixed set of parameter values) results in a probability distribution on graphs, and those that are maximum entropy within the considered class of distributions have the special property of being maximally unbiased null models for network inference [2] (e.g. biological network inference).
According to a 2018 publication by Zenil et al. there are several formulations by which to calculate network entropy and, as a rule, they all require a particular property of the graph to be focused, such as the adjacency matrix, degree sequence, degree distribution or number of bifurcations, what might lead to values of entropy that aren't invariant to the chosen network description.
where ,,, is a probability distribution. Since p 1 + p 2 + p 3 + p 4 = 1 {\displaystyle p_{1}+p_{2}+p_{3}+p_{4}=1} , a Bell diagonal state is determined by three real parameters. The maximum probability of a Bell diagonal state is defined as p m a x = max { p 1 , p 2 , p 3 , p 4 } {\displaystyle p_{max}=\max\{p_{1},p_{2},p_{3},p_{4}\}} .
Molecular dynamics generates a path as a set of (r t, p t) at discrete times t in [0,T] where T is the length of the path. For a transition from A to B, (r 0, p 0) is in A, and (r T, p T) is in B. One of the path times is chosen at random, the momenta p are modified slightly into p + δp, where δp is a random perturbation consistent with ...
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Watts–Strogatz small-world model generated by igraph and visualized by Cytoscape 2.5. 100 nodes. The Watts–Strogatz model is a random graph generation model that produces graphs with small-world properties, including short average path lengths and high clustering.