
Recognizing Graph Search Trees
Graph searches and the corresponding search trees can exhibit important ...
read it

Compacted binary trees admit a stretched exponential
A compacted binary tree is a directed acyclic graph encoding a binary tr...
read it

A structural characterization of treebased phylogenetic networks
Attempting to recognize a tree inside a network is a fundamental underta...
read it

Weakly displaying trees in temporal treechild network
Recently there has been considerable interest in the problem of finding ...
read it

A structure theorem for treebased phylogenetic networks
Attempting to recognize a tree inside a phylogenetic network is a fundam...
read it

Efficient Projection onto the Perfect Phylogeny Model
Several algorithms build on the perfect phylogeny model to infer evoluti...
read it

Sidestepping the Triangulation Problem in Bayesian Net Computations
This paper presents a new approach for computing posterior probabilities...
read it
Isomorphic unordered labeled trees up to substitution ciphering
Given two messages  as linear sequences of letters, it is immediate to determine whether one can be transformed into the other by simple substitution cipher of the letters. On the other hand, if the letters are carried as labels on nodes of topologically isomorphic unordered trees, determining if a substitution exists is referred to as marked tree isomorphism problem in the literature and has been show to be as hard as graph isomorphism. While the lefttoright direction provides the cipher of letters in the case of linear messages, if the messages are carried by unordered trees, the cipher is given by a tree isomorphism. The number of isomorphisms between two trees is roughly exponential in the size of the trees, which makes the problem of finding a cipher difficult by exhaustive search. This paper presents a method that aims to break the combinatorics of the isomorphisms search space. We show that in a linear time (in the size of the trees), we reduce the cardinality of this space by an exponential factor on average.
READ FULL TEXT
Comments
There are no comments yet.