04388nam 22007455 450 991030039910332120200702180400.03-319-06391-X10.1007/978-3-319-06391-1(CKB)3710000000119125(EBL)1731123(OCoLC)884013962(SSID)ssj0001239992(PQKBManifestationID)11810200(PQKBTitleCode)TC0001239992(PQKBWorkID)11205871(PQKB)11281435(MiAaPQ)EBC1731123(DE-He213)978-3-319-06391-1(PPN)178785377(EXLCZ)99371000000011912520140528d2014 u| 0engur|n|---|||||txtccrAlgorithms and Dynamical Models for Communities and Reputation in Social Networks /by Vincent Traag1st ed. 2014.Cham :Springer International Publishing :Imprint: Springer,2014.1 online resource (237 p.)Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053"Doctoral Thesis accepted by the Catholic University of Louvain, Belgium."3-319-06390-1 Includes bibliographical references at the end of each chapters and index.Introduction -- Part I Communities in Networks -- Community Detection -- Scale Invariant Community Detection -- Finding Significant Resolutions -- Modularity with Negative Links -- Applications -- Part II Social Balance & Reputation -- Social Balance -- Models of Social Balance -- Evolution of Cooperation -- Ranking Nodes Using Reputation.A persistent problem when finding communities in large complex networks is the so-called resolution limit. This thesis addresses this issue meticulously, and introduces the important notion of resolution-limit-free. Remarkably, only few methods possess this desirable property, and this thesis puts forward one such method. Moreover, it discusses how to asses whether communities can occur by chance or not. One aspect that is often ignored in this field is treated here: links can also be negative, as in war or conflict. Besides how to incorporate this in community detection, it also examines the dynamics of such negative links, inspired by a sociological theory known as social balance. This has intriguing connections to the evolution of cooperation, suggesting that for cooperation to emerge, groups often split in two opposing factions. In addition to these theoretical contributions, the thesis also contains an empirical analysis of the effect of trading communities on international conflict, and how communities form in a citation network with positive and negative links.Springer Theses, Recognizing Outstanding Ph.D. Research,2190-5053PhysicsGame theoryEconomicsSociological aspectsMathematicsSocial sciencesApplications of Graph Theory and Complex Networkshttps://scigraph.springernature.com/ontologies/product-market-codes/P33010Game Theory, Economics, Social and Behav. Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/M13011Organizational Studies, Economic Sociologyhttps://scigraph.springernature.com/ontologies/product-market-codes/X22020Mathematics in the Humanities and Social Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/M32000Physics.Game theory.EconomicsSociological aspects.Mathematics.Social sciences.Applications of Graph Theory and Complex Networks.Game Theory, Economics, Social and Behav. Sciences.Organizational Studies, Economic Sociology.Mathematics in the Humanities and Social Sciences.006.312Traag Vincentauthttp://id.loc.gov/vocabulary/relators/aut791836MiAaPQMiAaPQMiAaPQBOOK9910300399103321Algorithms and Dynamical Models for Communities and Reputation in Social Networks1770487UNINA03951nam 22006495 450 991059103770332120251113183018.09783031019609303101960110.1007/978-3-031-01960-9(MiAaPQ)EBC7080207(Au-PeEL)EBL7080207(CKB)24778996500041(PPN)264953797(OCoLC)1344432824(DE-He213)978-3-031-01960-9(EXLCZ)992477899650004120220902d2022 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierHigh-Performance Algorithms for Mass Spectrometry-Based Omics /by Fahad Saeed, Muhammad Haseeb1st ed. 2022.Cham :Springer International Publishing :Imprint: Springer,2022.1 online resource (146 pages)Computational Biology,2662-2432Print version: Saeed, Fahad High-Performance Algorithms for Mass Spectrometry-Based Omics Cham : Springer International Publishing AG,c2022 9783031019593 Includes bibliographical references and index.1. Need for High Performance Computing for Big MS Data -- 2. Introduction to Mass Spectrometry Data -- 3. A Review of Spectral Pre-processing -- 4. MS-REDUCE: An Ultra Data Reduction Algorithm -- 5. GPU-DAEMON: A Template to Support Development of GPU Algorithms -- 6. GPU-ArraySort: GPU Based Array Sorting Technique -- 7. G-MSR: A GPU Based Dimensionality Reduction Algorithm -- 8. Simulator Driven Proteomics -- 9. Future and Proposed Work.To date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that focuses on scalability of MS based methods. Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. In the recent years the technology has rapidly evolved and is now capable of generating increasingly large (multiple tera-bytes per experiment) and complex (multiple species/microbiome/high-dimensional) data sets. This rapid advance in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational resources available in a ubiquitous fashion in the form of multicore, manycore, CPU-GPU, CPU-FPGA, and IntelPhi architectures. The absence of these high-performance computing algorithms now hinders scientific advancements for mass spectrometry research. In this book we illustrate the need for high-performance computing algorithms for MS based proteomics, and proteogenomics and showcase our progress in developing these high-performance algorithms.Computational Biology,2662-2432BioinformaticsMass spectrometryComputer scienceComputational and Systems BiologyMass SpectrometryTheory and Algorithms for Application DomainsComputer ScienceBioinformatics.Mass spectrometry.Computer science.Computational and Systems Biology.Mass Spectrometry.Theory and Algorithms for Application Domains.Computer Science.005.1Saeed Fahad1255809Haseeb MuhammadMiAaPQMiAaPQMiAaPQBOOK9910591037703321High-performance algorithms for mass spectrometry-based omics3363948UNINA