280,439 research outputs found
On dynamic threshold graphs and related classes
This paper deals with the well known classes of threshold and difference graphs, both characterized by separators, i.e. node weight functions and thresholds. We design an efficient algorithm to find the minimum separator, and we show how to maintain minimum its value when the input (threshold or difference) graph is fully dynamic, i.e. edges/nodes are inserted/removed. Moreover, exploiting the data structure used for maintaining the minimality of the separator, we study the disjoint union and the join of two threshold graphs, showing that the resulting graphs are threshold signed graphs, i.e. a superclass of both threshold and difference graphs. Finally, we consider the complement operation on all the three introduced classes of graphs.
All these operations produce in output the modified graph in terms of their separator and require time linear w.r.t. the number of different degrees. We observe that recomputing from scratch the separator would run either in linear (for threshold and difference graphs) or quadratic (for threshold signed graphs) time w.r.t. the number of nodes of the graph
Counting approximately-shortest paths in directed acyclic graphs
Given a directed acyclic graph with positive edge-weights, two vertices s and
t, and a threshold-weight L, we present a fully-polynomial time
approximation-scheme for the problem of counting the s-t paths of length at
most L. We extend the algorithm for the case of two (or more) instances of the
same problem. That is, given two graphs that have the same vertices and edges
and differ only in edge-weights, and given two threshold-weights L_1 and L_2,
we show how to approximately count the s-t paths that have length at most L_1
in the first graph and length at most L_2 in the second graph. We believe that
our algorithms should find application in counting approximate solutions of
related optimization problems, where finding an (optimum) solution can be
reduced to the computation of a shortest path in a purpose-built auxiliary
graph
The Total Acquisition Number of Random Graphs
Let be a graph in which each vertex initially has weight 1. In each step,
the weight from a vertex can be moved to a neighbouring vertex ,
provided that the weight on is at least as large as the weight on . The
total acquisition number of , denoted by , is the minimum possible
size of the set of vertices with positive weight at the end of the process.
LeSaulnier, Prince, Wenger, West, and Worah asked for the minimum value of
such that with high probability, where
is a binomial random graph. We show that is a sharp threshold for this
property. We also show that almost all trees satisfy ,
confirming a conjecture of West.Comment: 18 pages, 1 figur
On the Mixing Time of Geographical Threshold Graphs
We study the mixing time of random graphs in the -dimensional toric unit
cube generated by the geographical threshold graph (GTG) model, a
generalization of random geometric graphs (RGG). In a GTG, nodes are
distributed in a Euclidean space, and edges are assigned according to a
threshold function involving the distance between nodes as well as randomly
chosen node weights, drawn from some distribution. The connectivity threshold
for GTGs is comparable to that of RGGs, essentially corresponding to a
connectivity radius of . However, the degree distributions
at this threshold are quite different: in an RGG the degrees are essentially
uniform, while RGGs have heterogeneous degrees that depend upon the weight
distribution. Herein, we study the mixing times of random walks on
-dimensional GTGs near the connectivity threshold for . If the
weight distribution function decays with for an arbitrarily small constant then the mixing time
of GTG is \mixbound. This matches the known mixing bounds for the
-dimensional RGG
A weighted configuration model and inhomogeneous epidemics
A random graph model with prescribed degree distribution and degree dependent
edge weights is introduced. Each vertex is independently equipped with a random
number of half-edges and each half-edge is assigned an integer valued weight
according to a distribution that is allowed to depend on the degree of its
vertex. Half-edges with the same weight are then paired randomly to create
edges. An expression for the threshold for the appearance of a giant component
in the resulting graph is derived using results on multi-type branching
processes. The same technique also gives an expression for the basic
reproduction number for an epidemic on the graph where the probability that a
certain edge is used for transmission is a function of the edge weight. It is
demonstrated that, if vertices with large degree tend to have large (small)
weights on their edges and if the transmission probability increases with the
edge weight, then it is easier (harder) for the epidemic to take off compared
to a randomized epidemic with the same degree and weight distribution. A recipe
for calculating the probability of a large outbreak in the epidemic and the
size of such an outbreak is also given. Finally, the model is fitted to three
empirical weighted networks of importance for the spread of contagious diseases
and it is shown that can be substantially over- or underestimated if the
correlation between degree and weight is not taken into account
Linear Programming Decoding of Spatially Coupled Codes
For a given family of spatially coupled codes, we prove that the LP threshold
on the BSC of the graph cover ensemble is the same as the LP threshold on the
BSC of the derived spatially coupled ensemble. This result is in contrast with
the fact that the BP threshold of the derived spatially coupled ensemble is
believed to be larger than the BP threshold of the graph cover ensemble as
noted by the work of Kudekar et al. (2011, 2012). To prove this, we establish
some properties related to the dual witness for LP decoding which was
introduced by Feldman et al. (2007) and simplified by Daskalakis et al. (2008).
More precisely, we prove that the existence of a dual witness which was
previously known to be sufficient for LP decoding success is also necessary and
is equivalent to the existence of certain acyclic hyperflows. We also derive a
sublinear (in the block length) upper bound on the weight of any edge in such
hyperflows, both for regular LPDC codes and for spatially coupled codes and we
prove that the bound is asymptotically tight for regular LDPC codes. Moreover,
we show how to trade crossover probability for "LP excess" on all the variable
nodes, for any binary linear code.Comment: 37 pages; Added tightness construction, expanded abstrac
Maximal entropy random networks with given degree distribution
Using a maximum entropy principle to assign a statistical weight to any
graph, we introduce a model of random graphs with arbitrary degree distribution
in the framework of standard statistical mechanics. We compute the free energy
and the distribution of connected components. We determine the size of the
percolation cluster above the percolation threshold. The conditional degree
distribution on the percolation cluster is also given. We briefly present the
analogous discussion for oriented graphs, giving for example the percolation
criterion.Comment: 22 pages, LateX, no figur
Isomorphisms in co-TT graphs
2019 Spring.Includes bibliographical references.A threshold tolerance graph is a graph where each vertex v is assigned a weight wv and a tolerance tv, and there is an edge between two vertices vx and vy if and only if wx + wy ≥ min(tx,ty). A co-TT graph is the complement of a threshold tolerance graph. Recognition of these graphs can be done in O(n2) time; however no polynomial-time algorithm to identify isomorphisms between pairs of TT or co-TT graphs was previously known. We give an algorithm to identify these isomorphisms, which takes O(n2) time
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