04692nam 22006855 450 991033787220332120200704080815.03-030-14683-910.1007/978-3-030-14683-2(CKB)4100000008160613(MiAaPQ)EBC5776051(DE-He213)978-3-030-14683-2(PPN)236523902(EXLCZ)99410000000816061320190513d2019 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierDynamics On and Of Complex Networks III Machine Learning and Statistical Physics Approaches /edited by Fakhteh Ghanbarnejad, Rishiraj Saha Roy, Fariba Karimi, Jean-Charles Delvenne, Bivas Mitra1st ed. 2019.Cham :Springer International Publishing :Imprint: Springer,2019.1 online resource (225 pages)Springer Proceedings in Complexity,2213-86843-030-14682-0 Part1. Network Structure -- Chapter1. An Empirical Study of the Effect of Noise Models on Centrality Metrics -- Chapter2. Emergence and Evolution of Hierarchical Structure in Complex Systems -- Chapter3. Evaluation of Cascading Infrastructure Failures and Optimal Recovery from a Network Science Perspective -- Part2. Network Dynamics -- Chapter4. Automatic Discovery of Families of Network Generative Processes -- Chapter5. Modeling User Dynamics in Collaboration Websites -- Chapter6. The Problem of Interaction Prediction in Link Streams -- Chapter7. The Network Source Location Problem in the Context of Foodborne Disease Outbreaks -- Part3. Theoretical Models and applications -- Chapter8. Network Representation Learning using Local Sharing and Distributed Graph Factorization (LSDGF) -- Chapter9. The Anatomy of Reddit: An Overview of Academic Research -- Chapter10. Learning Information Dynamics in Social Media: A Temporal Point Process Perspective. .This book bridges the gap between advances in the communities of computer science and physics--namely machine learning and statistical physics. It contains diverse but relevant topics in statistical physics, complex systems, network theory, and machine learning. Examples of such topics are: predicting missing links, higher-order generative modeling of networks, inferring network structure by tracking the evolution and dynamics of digital traces, recommender systems, and diffusion processes. The book contains extended versions of high-quality submissions received at the workshop, Dynamics On and Of Complex Networks (doocn.org), together with new invited contributions. The chapters will benefit a diverse community of researchers. The book is suitable for graduate students, postdoctoral researchers and professors of various disciplines including sociology, physics, mathematics, and computer science.Springer Proceedings in Complexity,2213-8684SociophysicsEconophysicsComputational complexitySocial sciences—Data processingSocial sciences—Computer programsSystem theoryData-driven Science, Modeling and Theory Buildinghttps://scigraph.springernature.com/ontologies/product-market-codes/P33030Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/T11022Computational Social Scienceshttps://scigraph.springernature.com/ontologies/product-market-codes/X34000Complex Systemshttps://scigraph.springernature.com/ontologies/product-market-codes/M13090Sociophysics.Econophysics.Computational complexity.Social sciences—Data processing.Social sciences—Computer programs.System theory.Data-driven Science, Modeling and Theory Building.Complexity.Computational Social Sciences.Complex Systems.005.8004.6Ghanbarnejad Fakhtehedthttp://id.loc.gov/vocabulary/relators/edtSaha Roy Rishirajedthttp://id.loc.gov/vocabulary/relators/edtKarimi Faribaedthttp://id.loc.gov/vocabulary/relators/edtDelvenne Jean-Charlesedthttp://id.loc.gov/vocabulary/relators/edtMitra Bivasedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910337872203321Dynamics On and Of Complex Networks III2529924UNINA