The traveling salesman problem on an n-point convex polygon and the minimum latency tour problem for n points on a straight line are two basic problems in graph theory and have been studied in the past. Previously, it...
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The traveling salesman problem on an n-point convex polygon and the minimum latency tour problem for n points on a straight line are two basic problems in graph theory and have been studied in the past. Previously, it was known that both problems can be solved in O(n(2)) time. However, whether they can be solved in o(n(2)) time was left open by Marcotte and Suri [SIAM J. Comput., 20 (1991), pp. 405-422] and Afrati et al. [Informatique Theorique Appl., 20 (1986), pp. 79-87], respectively. In this paper we show that both problems can be solved in O(n log n) time by reducing them to the following problem: Given an edge-weighted complete bipartite digraph G = (X, Y, E) with X = {x(0),..., x(n)} and Y = {y(0),..., y(m)}, we wish to find the shortest path from x(0) to x(n) in G. This new problem requires Omega(nm) time to solve in general, but we show that it can be solved in O(n + m log n) time if the weight matrices A and B of G are both concave, where for 0 less than or equal to i less than or equal to n and 0 less than or equal to j less than or equal to m, A[i, j] and B[j, i] are the weights of the edges (x(i), y(j)) and (y(j), x(i)) in G, respectively. As demonstrated in this paper, the new problem is a powerful tool and we believe that it can be used to solve more problems.
Communication networks are ubiquitous, increasingly complex, and dynamic. Predicting and visualizing common patterns in such a huge graph data of communication network is an essential task to understand active pattern...
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Communication networks are ubiquitous, increasingly complex, and dynamic. Predicting and visualizing common patterns in such a huge graph data of communication network is an essential task to understand active patterns evolved in the network. In this work, the problem is to find an active pattern in a communication network which is modeled as detection of a maximal common induced subgraph (CIS). The state of the communication network is captured as a time series of graphs which has periodic snapshots of logical communications within the network. A new centrality measure is proposed to assess the variation in successive graphs and to identify the behavior of each node in the time series graph. It extents help in the process of selecting a suitable candidate vertex for maximality in each step of the proposed algorithm. This paper is a pioneer attempt in using centrality measures to detect a maximal CIS of the huge graph database, which gives promising effect in the resultant graph in terms of large number of vertices. The algorithm has polynomial time complexity, and the efficiency of the algorithm is demonstrated by a series of experiments with synthetic graph datasets of different orders. The performance of real-time datasets further ensured the competence of the proposed algorithm.
We consider the WEAK ROMAN DOMINATION problem. Given an undirected graph G = (V, E), the aim is to find a weak Roman domination function (wrd-function for short) of minimum cost, i.e. a function f : V -> {0, 1, 2} ...
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We consider the WEAK ROMAN DOMINATION problem. Given an undirected graph G = (V, E), the aim is to find a weak Roman domination function (wrd-function for short) of minimum cost, i.e. a function f : V -> {0, 1, 2} such that every vertex v is an element of V is defended (i.e. there exists a neighbor u of v, possibly u = v, such that f (u) >= 1) and for every vertex v is an element of V with f (v) = 0 there exists a neighbor u of v such that f (u) >= 1 and the function f(u -> v) defined by f(u -> v)(v) = 1, f(u -> v)(u) = f (u) - 1 and f(u -> v)(x) = f(x) otherwise does not contain any undefended vertex. The cost of a wrd-function f is defined by cost(f) = Sigma(v is an element of V)f(v). The trivial enumeration algorithm runs in time O*(3(n)) and polynomial space and is the best one known for the problem so far. We are breaking the trivial enumeration barrier by providing two faster algorithms: we first prove that the problem can be solved in O*(2(n)) time needing exponential space, and then describe an O*(2.2279(n)) algorithm using polynomial space. Our results rely on structural properties of a wrd-function, as well as on the best polynomial space algorithm for the RED-BLUE DOMINATING SET problem. Moreover we show that the problem can be solved in linear-time on interval graphs. (C) 2017 Elsevier B.V. All rights reserved.
Real-world road networks often contain turn penalties or forbidden turns. Standard shortest path algorithms do not take these into account. Several ways of dealing with such turn restrictions have been proposed. It ha...
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Real-world road networks often contain turn penalties or forbidden turns. Standard shortest path algorithms do not take these into account. Several ways of dealing with such turn restrictions have been proposed. It has remained unclear which method is most suitable, even though efficiency is very important in route planning. We present a computational experiment comparing these methods. We conclude with a guideline for choosing the right algorithm in a real-world application. (C) 2012 Elsevier B.V. All rights reserved.
Genome assembly asks to reconstruct an unknown string from many shorter substrings of it. Even though it is one of the key problems in Bioinformatics, it is generally lacking major theoretical advances. Its hardness s...
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Genome assembly asks to reconstruct an unknown string from many shorter substrings of it. Even though it is one of the key problems in Bioinformatics, it is generally lacking major theoretical advances. Its hardness stems both from practical issues (size and errors of real data), and from the fact that problem formulations inherently admit multiple solutions. Given these, at their core, most state-of-the-art assemblers are based on finding non-branching paths (unitigs) in an assembly graph. While such paths constitute only partial assemblies, they are likely to be correct. More precisely, if one defines a genome assembly solution as a closed arc-covering walk of the graph, then unitigs appear in all solutions, being thus safe partial solutions. Until recently, it was open what are all the safe walks of an assembly graph. Tomescu and Medvedev (RECOMB 2016) characterized all such safe walks (omnitigs), thus giving the first safe and complete genome assembly algorithm. Even though maximal omnitig finding was later improved to quadratic time by Cairo et al. (ACM Trans. algorithms 2019), it remained open whether the crucial linear-time feature of finding unitigs can be attained with omnitigs. We answer this question affirmatively, by describing a surprising O(m)-time algorithm to identify all maximal omnitigs of a graph with n nodes and m arcs, notwithstanding the existence of families of graphs with T(mn) total maximal omnitig size. This is based on the discovery of a family of walks (macrotigs) with the property that all the non-trivial omnitigs are univocal extensions of subwalks of a macrotig. This has two consequences: (1) A linear-time output-sensitive algorithm enumerating all maximal omnitigs. (2) A compact O(m) representation of all maximal omnitigs, which allows, e.g., for O(m)-time computation of various statistics on them. Our results close a long-standing theoretical question inspired by practical genome assemblers, originating with the use of unitigs in 199
Health social networking communities are emerging resources for translational research. We have designed and implemented a framework called HyGen, which combines Semantic Web technologies, graph algorithms and user pr...
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Health social networking communities are emerging resources for translational research. We have designed and implemented a framework called HyGen, which combines Semantic Web technologies, graph algorithms and user profiling to discover and prioritize novel associations across disciplines. This manuscript focuses on the key strategies developed to overcome the challenges in handling patient-generated content in Health social networking communities. Heuristic and quantitative evaluations were carried out in colorectal cancer. The results demonstrate the potential of our approach to bridge silos and to identify hidden links among clinical observations, drugs, genes and diseases. In Amyotrophic Lateral Sclerosis case studies, HyGen has identified 15 of the 20 published disease genes. Additionally, HyGen has highlighted new candidates for future investigations, as well as a scientifically meaningful connection between riluzole and alcohol abuse. (C) 2011 Elsevier Inc. All rights reserved.
In 1960, Ore found a simple sufficient condition for a graph to have a Hamiltonian cycle. We expose a heuristic algorithm, hidden in Ore's proof, which can be very effective in actually finding such a cycle. This ...
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In 1960, Ore found a simple sufficient condition for a graph to have a Hamiltonian cycle. We expose a heuristic algorithm, hidden in Ore's proof, which can be very effective in actually finding such a cycle. This algorithm is always reasonably efficient and suggests an easy proof that almost all graphs are Hamiltonian.
In this paper, we initiate the study of total liar's domination of a graph. A subset LaS dagger V of a graph G=(V,E) is called a total liar's dominating set of G if (i) for all vaV, |N (G) (v)a (c) L|a parts p...
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In this paper, we initiate the study of total liar's domination of a graph. A subset LaS dagger V of a graph G=(V,E) is called a total liar's dominating set of G if (i) for all vaV, |N (G) (v)a (c) L|a parts per thousand yen2 and (ii) for every pair u,vaV of distinct vertices, |(N (G) (u)a(a)N (G) (v))a (c) L|a parts per thousand yen3. The total liar's domination number of a graph G is the cardinality of a minimum total liar's dominating set of G and is denoted by gamma (TLR) (G). The Minimum Total Liar's Domination Problem is to find a total liar's dominating set of minimum cardinality of the input graph G. Given a graph G and a positive integer k, the Total Liar's Domination Decision Problem is to check whether G has a total liar's dominating set of cardinality at most k. In this paper, we give a necessary and sufficient condition for the existence of a total liar's dominating set in a graph. We show that the Total Liar's Domination Decision Problem is NP-complete for general graphs and is NP-complete even for split graphs and hence for chordal graphs. We also propose a 2(ln Delta(G)+1)-approximation algorithm for the Minimum Total Liar's Domination Problem, where Delta(G) is the maximum degree of the input graph G. We show that Minimum Total Liar's Domination Problem cannot be approximated within a factor of for any I mu > 0, unless NPaS dagger DTIME(|V|(loglog|V|)). Finally, we show that Minimum Total Liar's Domination Problem is APX-complete for graphs with bounded degree 4.
In a graph, a matching cut is an edge cut that is a matching. MATCHING CUT is the problem of deciding whether or not a given graph has a matching cut, which is known to be NP-complete. This paper provides a first bran...
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In a graph, a matching cut is an edge cut that is a matching. MATCHING CUT is the problem of deciding whether or not a given graph has a matching cut, which is known to be NP-complete. This paper provides a first branching algorithm solving MATCHING Cur in time O*(2(n/2)) = O*(1.4143(n)) for an n-vertex input graph, and shows that MATCHING CUT parameterized by the vertex cover number tau(G) can be solved by a single-exponential algorithm in time 2 tau((G)) O(n(2)). Moreover, the paper also gives a polynomially solvable case for MATCHING Cur which covers previous known results on graphs of maximum degree three, line graphs, and claw-free graphs. (C) 2015 Elsevier B.V. All rights reserved.
A new algorithm is given to find a maximum flow in an undirected planar flow network in $O(n\log ^2 n)$ time, which is faster than the best method previously known by a factor of $\sqrt n /\log n$. The algorithm const...
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A new algorithm is given to find a maximum flow in an undirected planar flow network in $O(n\log ^2 n)$ time, which is faster than the best method previously known by a factor of $\sqrt n /\log n$. The algorithm constructs a transformation of the dual of the given flow network in which differences between shortest distances are equal, under suitable edge correspondences, to edge flows in the given network. The transformation depends on the value of a maximum flow. The algorithm then solves the shortest distances problem efficiently by exploiting certain structural properties of the transformed dual, as well as using a set of cuts constructible in $O(n\log ^2 n)$ time by a known method which is also used to find the requisite flow value. The main result can be further improved by a factor of $\log n/\log^* n$ if a recently developed shortest path algorithm for planar networks is used in place of Dijkstra’s algorithm in each step where shortest paths are computed.
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