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001484272 003__ OCoLC
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001484272 019__ $$a1410493406$$a1414477020
001484272 020__ $$a9783031303999$$qelectronic book
001484272 020__ $$a3031303997$$qelectronic book
001484272 020__ $$z3031303989
001484272 020__ $$z9783031303982
001484272 0247_ $$a10.1007/978-3-031-30399-9$$2doi
001484272 035__ $$aSP(OCoLC)1410591673
001484272 040__ $$aEBLCP$$beng$$erda$$cEBLCP$$dGW5XE$$dYDX$$dOCLCO$$dEBLCP$$dSFB
001484272 049__ $$aISEA
001484272 050_4 $$aQA402$$b.T46 2023
001484272 08204 $$a003.72$$223/eng/20231208
001484272 24500 $$aTemporal network theory /$$cPetter Holme, Jari Saramäki, editors.
001484272 250__ $$a2nd edition.
001484272 264_1 $$aCham :$$bSpringer,$$c2023.
001484272 300__ $$a1 online resource (486 p.).
001484272 336__ $$atext$$btxt$$2rdacontent
001484272 337__ $$acomputer$$bc$$2rdamedia
001484272 338__ $$aonline resource$$bcr$$2rdacarrier
001484272 4901_ $$aComputational Social Sciences
001484272 500__ $$aIncludes index.
001484272 5050_ $$aIntro -- Preface to the Second Edition -- Preface to the First Edition -- Contents -- 1 A Map of Approaches to Temporal Networks -- 1.1 Overview -- 1.2 Temporal Network Data -- 1.2.1 Events -- 1.2.2 Boundaries -- 1.2.3 Connectivity -- 1.3 Simplifying and Coarse-Graining Temporal Networks -- 1.3.1 Projections to Static Networks -- 1.3.2 Separating the Dynamics of Contacts, Links, and Nodes -- 1.3.3 Mesoscopic Structures -- 1.3.4 Fundamental Structures -- 1.4 Important Nodes, Links, and Events -- 1.4.1 Generalizing Centrality Measures -- 1.4.2 Controllability
001484272 5058_ $$a1.4.3 Vaccination, Sentinel Surveillance, and Influence Maximization -- 1.4.4 Robustness to Failure and Attack -- 1.5 How Structure Affects Dynamics -- 1.5.1 Simulating Disease Spreading -- 1.5.2 Tuning Temporal Network Structure by Randomization -- 1.5.3 Models of Temporal Networks -- 1.6 Other Topics -- 1.7 Future Perspectives -- References -- 2 Fundamental Structures in Temporal Communication Networks -- 2.1 Introduction -- 2.2 Network Structure of Communication Events -- 2.2.1 Synchronous Versus Asynchronous -- 2.2.2 One-to-One, One-to-Many, Many-to-Many -- 2.2.3 Connecting to Network Theory
001484272 5058_ $$a2.2.4 The Case of Many-to-Many, Synchronous Networks -- 2.3 Frequently Asked Questions -- 2.3.1 What Do You Mean `Framework'!? -- 2.3.2 Is the Framework All Done and Ready to Use? -- 2.3.3 Is It Just for Communication Networks? -- 2.3.4 Isn't All This Obvious? -- 2.4 Consequences for Analysis and Modeling -- 2.4.1 Randomization -- 2.4.2 Generative Models -- 2.4.3 Link Prediction and Link Activity -- 2.4.4 Spreading Processes -- 2.4.5 Communities -- 2.5 Conclusion -- References -- 3 Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks -- 3.1 Introduction
001484272 5058_ $$a3.2 Weighted Stream Graphs -- 3.3 Bipartite Stream Graphs -- 3.4 Directed Stream Graphs -- 3.5 Conclusion -- References -- 4 Modelling Temporal Networks with Markov Chains, Community Structures and Change Points -- 4.1 Introduction -- 4.2 Temporal Networks as Markov Chains -- 4.3 Markov Chains with Communities -- 4.4 Markov Chains with Change Points -- 4.5 Conclusion -- References -- 5 Visualisation of Structure and Processes on Temporal Networks -- 5.1 Introduction -- 5.2 Temporal Networks -- 5.3 Visualisation on and of Temporal Networks -- 5.3.1 Layouts -- 5.3.2 Visual Clutter
001484272 506__ $$aAccess limited to authorized users.
001484272 520__ $$aThis book focuses on the theoretical side of temporal network research and gives an overview of the state of the art in the field. Curated by two pioneers in the field who have helped to shape it, the book contains contributions from many leading researchers. Temporal networks fill the border area between network science and time-series analysis and are relevant for epidemic modeling, optimization of transportation and logistics, as well as understanding biological phenomena. Over the past 20 years, network theory has proven to be one of the most powerful tools for studying and analyzing complex systems. Temporal network theory is perhaps the most recent significant development in the field in recent years, with direct applications to many of the big data sets. This book appeals to students, researchers, and professionals interested in theory and temporal networksa field that has grown tremendously over the last decade. This second edition of Temporal Network Theory extends the first with three chapters highlighting recent developments in the interface with machine learning.
001484272 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed December 7, 2023).
001484272 650_0 $$aSystem analysis.
001484272 650_0 $$aComputational complexity.
001484272 650_6 $$aAnalyse de systèmes.
001484272 650_6 $$aComplexité de calcul (Informatique)
001484272 655_0 $$aElectronic books.
001484272 7001_ $$aHolme, Petter.
001484272 7001_ $$aSaramäki, Jari.
001484272 77608 $$iPrint version:$$aHolme, Petter$$tTemporal Network Theory$$dCham : Springer International Publishing AG,c2023
001484272 830_0 $$aComputational social sciences.
001484272 852__ $$bebk
001484272 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-30399-9$$zOnline Access$$91397441.1
001484272 909CO $$ooai:library.usi.edu:1484272$$pGLOBAL_SET
001484272 980__ $$aBIB
001484272 980__ $$aEBOOK
001484272 982__ $$aEbook
001484272 983__ $$aOnline
001484272 994__ $$a92$$bISE