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检索条件"主题词=Graph Algorithms"

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Distributed Node Election For Full Coverage Using Connectivity Information 4

Distributed Node Election For Full Coverage Using Connectivi...

引用

4th IEEE International Conference on Information Science and Technology (ICIST)

作者： Li, Xiaoyun Bleakley, Chris Chinese Acad Sci Shenzhen Inst Adv Technol Univ Town Peoples R China Univ Coll Dublin UCD Complex & Adapt Syst Lab UCD Sch Comp Sci & Informat Dublin 4 Ireland

ISBN: (纸本)9781479948086

This paper proposes a distributed node election algorithm to efficiently select a subset of nodes which provide full coverage. In addition the boundary of un-triangulated holes can be accurately detected. This algorithm only uses binary connectivity information without any additional information such as sensing range or distance between nodes. Assumptions of a Unit Disk graph (UDG) or d-quasi UDG network configuration are not required. The number of elected nodes is close to the minimum number of nodes for full coverage as shown in simulations with arbitrary topologies and various densities. The algorithm is scalable to any network size. Its time complexity is only sensitive to the density of the network (or average number of neighbors for each node) and is insensitive to the total number of nodes in the network.

关键词： graph algorithms Distributed Systems Network topology hole detection boundary detection coverage triangulation communication graph wireless sensor networks wireless communication MANET

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Can Quantum Communication Speed Up Distributed Computation? 14

Can Quantum Communication Speed Up Distributed Computation?

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ACM Symposium on Principles of Distributed Computing (PODC)

作者： Elkin, Michael Klauck, Hartmut Nanongkai, Danupon Pandurangan, Gopal Ben Gurion Univ Negev Dept Comp Sci Beer Sheva Israel Nanyang Technol Univ Singapore Singapore Ctr Quantum Technol Singapore Singapore Univ Vienna Fac Comp Sci Vienna Austria Brown Univ Providence RI 02912 USA

ISBN: (纸本)9781450329446

The focus of this paper is on quantum distributed computation, where we investigate whether quantum communication can help in speeding up distributed network algorithms. Our main result is that for certain fundamental network problems such as minimum spanning tree, minimum cut, and shortest paths, quantum communication does not help in substantially speeding up distributed algorithms for these problems compared to the classical setting. In order to obtain this result, we extend the technique of Das Sarma et al. [SICOMP 2012] to obtain a uniform approach to prove non-trivial lower bounds for quantum distributed algorithms for several graph optimization (both exact and approximate versions) as well as verification problems, some of which are new even in the classical setting, e.g. tight randomized lower bounds for Hamiltonian cycle and spanning tree verification, answering an open problem of Das Sarma et al., and a lower bound in terms of the weight aspect ratio, matching the upper bounds of Elkin [STOC 2004]. Our approach introduces the Server model and Quantum Simulation Theorem which together provide a connection between distributed algorithms and communication complexity. The Server model is the standard twoparty communication complexity model augmented with additional power;yet, most of the hardness in the two-party model is carried over to this new model. The Quantum Simulation Theorem carries this hardness further to quantum distributed computing. Our techniques, except the proof of the hardness in the Server model, require very little knowledge in quantum computing, and this can help overcoming a usual impediment in proving bounds on quantum distributed algorithms. In particular, if one can prove a lower bound for distributed algorithms for a certain problem using the technique of Das Sarma et al., it is likely that such lower bound can be extended to the quantum setting using tools provided in this paper and without the need of knowledge in quantum computing.

关键词： distributed computing graph algorithms quantum communication time complexity lower bound CONGEST model

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Complexity of colouring problems restricted to unichord-free and {square,unichord)-free graphs

Complexity of colouring problems restricted to unichord-free...

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1st International Symposium on Combinatorial Optimization (ISCO)

作者： Machado, Raphael C. S. de Figueiredo, Celina M. H. Trotignon, Nicolas Inmetro Rio De Janeiro RJ Brazil Univ Fed Rio de Janeiro COPPE Rio de Janeiro Brazil Univ Lyon INRIA UCBL CNRSLIPENS Lyon Lyon France

A unichord in a graph is an edge that is the unique chord of a cycle. A square is an induced cycle on four vertices. A graph is unichord free if none of its edges is a unichord. We give a slight restatement of a known structure theorem for unichord-free graphs and use it to show that, with the only exception of the complete graph k(4), every square-free, unichordfree graph of maximum degree 3 can be total-coloured with four colours. Our proof can be turned into a polynomial-time algorithm that actually outputs the colouring. This settles the class of square-free, unichord-free graphs as a class for which edge-colouring is NP-complete but total-colouring is polynomial. (C) 2012 Elsevier B.V. All rights reserved.

关键词： graph algorithms Complexity Colouring Unichord free

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MapReduce-based Distributed k-shell Decomposition for online Social Networks 10

MapReduce-based Distributed <i>k</i>-shell Decomposition for...

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IEEE World Congress on Services (SERVICES)

作者： Pechlivanidou, Katerina Katsaros, Dimitrios Tassiulas, Leandros CERTH Elect & Comp Engn Volos Greece Univ Thessaly Volos Greece

ISBN: (纸本)9781479950690

Social network analysis comprises a popular set of tools for the analysis of online social networks. Among these techniques, k-shell decomposition of a graph is a popular technique that has been used for centrality analysis, for communities discovery, for the detection of influential spreaders, and so on. The huge volume of input graphs and the environments where the algorithm needs to run i.e., large datacenters, makes none of the existing algorithms appropriate for the decomposition of graphs into shells. In this article, we develop for the first time in the literature, a distributed algorithm based on MapReduce for the k-shell decomposition of a graph. We furthermore, provide an implementation and assessment of the algorithm using real social network datasets. We analyze the tradeoffs and speedup of the proposed algorithm and conclude for its virtues and shortcomings.

关键词： Map-Reduce Hadoop distributed algorithms k-shell decomposition graph algorithms social networks

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On CLIQUE Problem for Sparse graphs of Large Dimension 13

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13th International Scientific Conference on Information Technologies and Mathematical Modelling (ITMM)

作者： Bykova, Valentina Illarionov, Roman Siberian Fed Univ Inst Math & Comp Sci Krasnoyarsk 660041 Russia

ISBN: (纸本)9783319136714;9783319136707

In this paper the NP-hard Maximum Clique Problem (MCP) is considered. It is supposed that the input graph is sparse. Also, it is believed that the input graph can have a huge number of vertices. A biphasic algorithm for finding the exact solution of the MCP is proposed in the paper. The first phase of the algorithm is the preprocessing of the input graph by decomposing it into atoms. The second phase of the algorithm reduces to an application for each atom classical algorithm Wilf and then to the formation of solutions for the graph as a whole. The level of sparseness of the input graph is given in the form of restrictions on its treewidth. It have been proved that the running time of biphasic algorithm is polynomial in the number of vertices and the exponential of the treewidth of the input graph.

关键词： graph algorithms sparse graphs decomposition graph atom graph preprocessing biphasic algorithms treewidth FPT-algorithms

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Locally Estimating Core Numbers 14

Locally Estimating Core Numbers

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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： O'Brien, Michael P. Sullivan, Blair D. North Carolina State Univ Dept Comp Sci Raleigh NC 27607 USA

ISBN: (纸本)9781479943036

graphs are a powerful way to model interactions and relationships in data from a wide variety of application domains. In this setting, entities represented by vertices at the "center" of the graph are often more important than those associated with vertices on the "fringes". For example, central nodes tend to be more critical in the spread of information or disease and play an important role in clustering/community formation. Identifying such "core" vertices has recently received additional attention in the context of network experiments, which analyze the response when a random subset of vertices are exposed to a treatment (e.g. inoculation, free product samples, etc). Specifically, the likelihood of having many central vertices in any exposure subset can have a significant impact on the experiment. We focus on using k-cores and core numbers to measure the extent to which a vertex is central in a graph. Existing algorithms for computing the core number of a vertex require the entire graph as input, an unrealistic scenario in many real world applications. Moreover, in the context of network experiments, the subgraph induced by the treated vertices is only known in a probabilistic sense. We introduce a new method for estimating the core number based only on the properties of the graph within a region of radius delta around the vertex, and prove an asymptotic error bound of our estimator on random graphs. Further, we empirically validate the accuracy of our estimator for small values of delta on a representative corpus of real data sets. Finally, we evaluate the impact of improved local estimation on an open problem in network experimentation posed by Ugander et al.

关键词： computational complexity graph theory network theory (graphs) set theory application domains asymptotic error bound central nodes central vertices clustering formation community formation core vertices data interactions data relationships empirical analysis exposure subset graph fringes graph properties graph vertices k-cores local core number estimation network experiments probability radius region random graphs random vertex subset real data sets real world applications response analysis subgraphs time complexity unrealistic scenario Accuracy Communities Computational complexity Data models Equations Estimation Upper bound core numbers graph algorithms

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Deterministic Stateless Centralized Local algorithms for Bounded Degree graphs

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22nd Annual European Symposium on algorithms (ESA) held as part of ALGO Meeting

作者： Even, Guy Medina, Moti Ron, Dana Tel Aviv Univ Sch Elect Engn IL-69978 Tel Aviv Israel

ISBN: (纸本)9783662447772;9783662447765

We design centralized local algorithms for: maximal independent set, maximal matching, and graph coloring. The improvement is threefold: the algorithms are deterministic, stateless, and the number of probes is O(log* n), where n is the number of vertices of the input graph. Our algorithms for maximal independent set and maximal matching improves over previous randomized algorithms by Alon et al. (SODA 2012) and Mansour et al. (ICALP 2012). In these previous algorithms, the number of probes and the space required for storing the state between queries are poly(log n). We also design the first centralized local algorithm for graph coloring. Our graph coloring algorithms are deterministic and stateless. Let Delta denote the maximum degree of a graph over n vertices. Our algorithm for coloring the vertices by Delta + 1 colors requires O(log* n) probes for constant degree graphs. Surprisingly, for the case where the number of colors is O(Delta(2) log.), the number of probes of our algorithm is O(Delta . log* n + Delta(2)), that is, the number of probes is sublinear if Delta = o(root n), i.e., our algorithm applies for graphs with unbounded degrees.

关键词： Centralized Local algorithms Sublinear Approximation algorithms graph algorithms

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Scalable and High Performance Betweenness Centrality on the GPU 14

Scalable and High Performance Betweenness Centrality on the ...

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International Conference on High Performance Computing, Networking, Storage and Analysis

作者： McLaughlin, Adam Bader, David A. Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA

ISBN: (纸本)9781479955008

graphs that model social networks, numerical simulations, and the structure of the Internet are enormous and cannot be manually inspected. A popular metric used to analyze these networks is betweenness centrality, which has applications in community detection, power grid contingency analysis, and the study of the human brain. However, these analyses come with a high computational cost that prevents the examination of large graphs of interest. Prior GPU implementations suffer from large local data structures and inefficient graph traversals that limit scalability and performance. Here we present several hybrid GPU implementations, providing good performance on graphs of arbitrary structure rather than just scale-free graphs as was done previously. We achieve up to 13x speedup on high-diameter graphs and an average of 2.71x speedup overall over the best existing GPU algorithm. We observe near linear speedup and performance exceeding tens of GTEPS when running betweenness centrality on 192 GPUs.

关键词： GPUs graph algorithms Parallel algorithms

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High Quality, Scalable and Parallel Community Detection for Large Real graphs 14

High Quality, Scalable and Parallel Community Detection for ...

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23rd International Conference on World Wide Web (WWW)

作者： Prat-Perez, Arnau Dominguez-Sal, David Larriba-Pey, Josep-LLuis UPC DAMA Barcelona Spain Spars Technol Barcelona Spain

ISBN: (纸本)9781450327442

Community detection has arisen as one of the most relevant topics in the field of graph mining, principally for its applications in domains such as social or biological networks analysis. Different community detection algorithms have been proposed during the last decade, approaching the problem from different perspectives. However, existing algorithms are, in general, based on complex and expensive computations, making them unsuitable for large graphs with millions of vertices and edges such as those usually found in the real world. In this paper, we propose a novel disjoint community detection algorithm called Scalable Community Detection (SCD). By combining different strategies, SCD partitions the graph by maximizing the Weighted Community Clustering (WCC), a recently proposed community detection metric based on triangle analysis. Using real graphs with ground truth overlapped communities, we show that SCD outperforms the current state of the art proposals (even those aimed at finding overlapping communities) in terms of quality and performance. SCD provides the speed of the fastest algorithms and the quality in terms of NMI and F1Score of the most accurate state of the art proposals. We show that SCD is able to run up to two orders of magnitude faster than practical existing solutions by exploiting the parallelism of current multi-core processors, enabling us to process graphs of unprecedented size in short execution times.

关键词： graph algorithms Community Detection Clustering Parallel Social Networks graph Partition Modularity WCC

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Finding Motifs in Biological Sequences using the Micron Automata Processor

Finding Motifs in Biological Sequences using the Micron Auto...

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IEEE 28th International Parallel & Distributed Processing Symposium (IPDPS)

作者： Roy, Indranil Aluru, Srinivas Georgia Inst Technol Sch Computat Sci & Engn Atlanta GA 30332 USA

ISBN: (纸本)9780769552071

Finding approximately conserved sequences, called motifs, across multiple DNA or protein sequences is an important problem in computational biology. In this paper, we consider the (l, d) motif search problem of identifying one or more motifs of length l present in at least q of the n given sequences, with each occurrence differing from the motif in at most d substitutions. The problem is known to be NP-hard, and the largest solved instance reported to date is (26, 11). We propose a novel algorithm for the (l, d) motif search problem using streaming execution over a large set of Non-deterministic Finite Automata (NFA). This solution is designed to take advantage of the Micron Automata Processor, a new technology close to deployment that can simultaneously execute multiple NFA in parallel. We estimate the run-time for the (39, 18) and (40, 17) problem instances using the resources available within a single Automata Processor board. In addition to solving larger instances of the (l, d) motif search problem, the paper serves as a useful guide to solving problems using this new accelerator technology.

关键词： computational biology finite automaton graph algorithms hardware acceleration motif detection

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