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Dynamics on and of complex networks ...
~
Ghanbarnejad, Fakhteh.
Dynamics on and of complex networks IIImachine learning and statistical physics approaches /
Record Type:
Electronic resources : Monograph/item
Title/Author:
Dynamics on and of complex networks IIIedited by Fakhteh Ghanbarnejad ... [et al.].
Reminder of title:
machine learning and statistical physics approaches /
other author:
Ghanbarnejad, Fakhteh.
Published:
Cham :Springer International Publishing :2019.
Description:
x, 244 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
Subject:
Computer networks.
Online resource:
https://doi.org/10.1007/978-3-030-14683-2
ISBN:
9783030146832$q(electronic bk.)
Dynamics on and of complex networks IIImachine learning and statistical physics approaches /
Dynamics on and of complex networks III
machine learning and statistical physics approaches /[electronic resource] :edited by Fakhteh Ghanbarnejad ... [et al.]. - Cham :Springer International Publishing :2019. - x, 244 p. :ill., digital ;24 cm. - Springer proceedings in complexity,2213-8684. - Springer proceedings in complexity..
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.
ISBN: 9783030146832$q(electronic bk.)
Standard No.: 10.1007/978-3-030-14683-2doiSubjects--Topical Terms:
181923
Computer networks.
LC Class. No.: TK5105.5 / .D963 2019
Dewey Class. No.: 004.6
Dynamics on and of complex networks IIImachine learning and statistical physics approaches /
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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.
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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.
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EB TK5105.5 .D997 2019 2019
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https://doi.org/10.1007/978-3-030-14683-2
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