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22
ConstantTime Distributed Dominating Set Approximation
 In Proc. of the 22 nd ACM Symposium on the Principles of Distributed Computing (PODC
, 2003
"... Finding a small dominating set is one of the most fundamental problems of traditional graph theory. In this paper, we present a new fully distributed approximation algorithm based on LP relaxation techniques. For an arbitrary parameter k and maximum degree #, our algorithm computes a dominating set ..."
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Cited by 133 (22 self)
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Finding a small dominating set is one of the most fundamental problems of traditional graph theory. In this paper, we present a new fully distributed approximation algorithm based on LP relaxation techniques. For an arbitrary parameter k and maximum degree #, our algorithm computes a dominating set of expected size O k# log #DSOPT rounds where each node has to send O k messages of size O(log #). This is the first algorithm which achieves a nontrivial approximation ratio in a constant number of rounds.
SIGMA: A SETCOVERBASED INEXACT GRAPH MATCHING ALGORITHM
, 2010
"... Network querying is a growing domain with vast applications ranging from screening compounds against a database of known molecules to matching subnetworks across species. Graph indexing is a powerful method for searching a large database of graphs. Most graph indexing methods to date tackle the exa ..."
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Cited by 13 (3 self)
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Network querying is a growing domain with vast applications ranging from screening compounds against a database of known molecules to matching subnetworks across species. Graph indexing is a powerful method for searching a large database of graphs. Most graph indexing methods to date tackle the exact matching (isomorphism) problem, limiting their applicability to specific instances in which such matches exist. Here we provide a novel graph indexing method to cope with the more general, inexact matching problem. Our method, SIGMA, builds on approximating a variant of the setcover problem that concerns overlapping multisets. We extensively test our method and compare it to a baseline method and to the stateoftheart Grafil. We show that SIGMA outperforms both, providing higher pruning power in all the tested scenarios.
The Centrality of
, 1992
"... This Article is brought to you for free and open access by the Biochemistry, Department of at DigitalCommons@University of Nebraska Lincoln. It ..."
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Cited by 10 (4 self)
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This Article is brought to you for free and open access by the Biochemistry, Department of at DigitalCommons@University of Nebraska Lincoln. It
Maximizing output and recognizing autocatalysis in chemical reaction networks is npcomplete
 Journal of Systems Chemistry 2012
"... Background: A classical problem in metabolic design is to maximize the production of desired compound in a given chemical reaction network by appropriately directing the mass flow through the network. Computationally, this problem is addressed as a linear optimization problem over the “flux cone”. T ..."
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Cited by 8 (5 self)
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Background: A classical problem in metabolic design is to maximize the production of desired compound in a given chemical reaction network by appropriately directing the mass flow through the network. Computationally, this problem is addressed as a linear optimization problem over the “flux cone”. The prior construction of the flux cone is computationally expensive and no polynomialtime algorithms are known. Results: Here we show that the output maximization problem in chemical reaction networks is NPcomplete. This statement remains true even if all reactions are monomolecular or bimolecular and if only a single molecular species is used as influx. As a corollary we show, furthermore, that the detection of autocatalytic species, i.e., types that can only be produced from the influx material when they are present in the initial reaction mixture, is an NPcomplete computational problem. Conclusions: Hardness results on combinatorial problems and optimization problems are important to guide the development of computational tools for the analysis of metabolic networks in particular and chemical reaction networks in general. Our results indicate that efficient heuristics and approximate algorithms need to be employed for the analysis of large chemical networks since even conceptually simple flow problems are provably intractable.
An Ant Based Particle Swarm Optimization Algorithm for Maximum Clique
 Problem in Social Networks, in State of the Art Applications of Social Network Analysis, eds. F. Can, T. Özyer and F. Polat (Springer International Publishing
"... Abstract In recent years, social network services provide a suitable platform for analyzing the activity of users in social networks. In online social networks, interaction between users plays a key role in social network analysis. One of the important types of social structure is a full connected ..."
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Cited by 6 (4 self)
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Abstract In recent years, social network services provide a suitable platform for analyzing the activity of users in social networks. In online social networks, interaction between users plays a key role in social network analysis. One of the important types of social structure is a full connected relation between some users, which known as clique structure. Therefore finding a maximum clique is essential for analysis of certain groups and communities in social networks. This paper proposed a new hybrid method using ant colony optimization algorithm and particle swarm optimization algorithm for finding a maximum clique in social networks. In the proposed method, it is improved process of pheromone update by particle swarm optimization in order to attain better results. Simulation results on popular standard social network benchmarks in comparison standard ant colony optimization algorithm are shown a relative enhancement of proposed algorithm.
COMBINATORIAL RECONSTRUCTION OF HALFSIBLING GROUPS FROM MICROSATELLITE DATA
, 2009
"... While full sibling group reconstruction from microsatellite data is a well studied problem, reconstruction of half sibling groups is much less studied, theoretically challenging, and a computationally demanding problem. In this paper, we present a formulation of the halfsibling reconstruction probl ..."
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Cited by 5 (3 self)
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While full sibling group reconstruction from microsatellite data is a well studied problem, reconstruction of half sibling groups is much less studied, theoretically challenging, and a computationally demanding problem. In this paper, we present a formulation of the halfsibling reconstruction problem and prove it APXhardness. We also present exact solutions for this formulation and develop heuristics. Using biological and synthetic datasets we present experimental results and compare them with the leading alternative software COLONY. We show that our results are competitive and allow halfsibling group reconstruction in the presence of polygamy, which is prevalent in nature.
THINKING STRATEGICALLY ABOUT THINKING STRATEGICALLY: THE COMPUTATIONAL STRUCTURE AND DYNAMICS OF MANAGERIAL PROBLEM SELECTION AND FORMULATION
, 2009
"... A new model of managerial problem formulation is introduced and developed to answer the question: ‘What kinds of problems do strategic managers engage in solving and why? ’ The article proposes that a key decision metric for choosing among alternative problem statements is the computational complexi ..."
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Cited by 2 (0 self)
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A new model of managerial problem formulation is introduced and developed to answer the question: ‘What kinds of problems do strategic managers engage in solving and why? ’ The article proposes that a key decision metric for choosing among alternative problem statements is the computational complexity of the solution algorithm of alternative statements. Managerial problem statements are grouped into two classes on the basis of their computational complexity: Ptype problems (canonically easy ones) and NPtype problems (hard ones). The new model of managerial cognitive choice posits that managers prefer to engage with and solve Ptype problems over solving NPtype problems. The model explains common patterns of managerial reasoning and decision making, including many documented ‘biases ’ and simplifying heuristics, and points the way to new effects and novel empirical investigations of problem solvingoriented thinking in strategic management and types of generic strategies, driven by predictions about the kinds of market and industrylevel changes that managers will or will not respond to. Copyright © 2009 John Wiley & Sons, Ltd.
2.5. Navigating and Mining Connectomes
"... 3.2. ON cone BC drive in the OFF sublayer 3.3. OFF cone BC drive in the ON sublayer 3.4. ONOFF crossover motifs 3.5. Rodcone crossover suppression and winnertakeall networks ..."
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3.2. ON cone BC drive in the OFF sublayer 3.3. OFF cone BC drive in the ON sublayer 3.4. ONOFF crossover motifs 3.5. Rodcone crossover suppression and winnertakeall networks
unknown title
"... As analysts are expected to process a greater amount of information in a shorter amount of time, creators of big data software are challenged with the need for improved efficiency. Ray, our group’s usable, scalable genome assembler, addresses big data problems by using optimal resources and producin ..."
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As analysts are expected to process a greater amount of information in a shorter amount of time, creators of big data software are challenged with the need for improved efficiency. Ray, our group’s usable, scalable genome assembler, addresses big data problems by using optimal resources and producing one, correct and conservative, timely solution. Only by abstracting the size of the data from both the computers and the humans can the real scientific question, often complex in itself, eventually be solved. To draw a curtain over the specific computational machinery of big data, we developed RayPlatform, a programming framework that allows users to concentrate on their domainspecific problems. RayPlatform is a parallel messagepassing software framework that runs on clouds, supercomputers, and desktops alike. Using established technologies such as C ++ and MPI (messagepassing interface), we handle the genomes of hundreds of species, from viruses to plants, using machines ranging from desktop computers to supercomputers. From this experience, we present insights on making computer time more useful—and user time much more valuable.