عنوان

Dynamics on and of complex networks III :

(Number (ISBN

3030146839

(Number (ISBN

9783030146832

Erroneous ISBN

3030146820

Erroneous ISBN

9783030146825

Title Proper

Dynamics on and of complex networks III :

General Material Designation

[Book]

Other Title Information

machine learning and statistical physics approaches.

Place of Publication, Distribution, etc.

[Place of publication not identified] :

Name of Publisher, Distributor, etc.

SPRINGER NATURE,

Date of Publication, Distribution, etc.

2019.

Specific Material Designation and Extent of Item

1 online resource

Series Title

Springer Proceedings in Complexity

Text of Note

Includes bibliographical references and index.

Text of Note

Intro; Preface; List of Reviewers (Alphabetically Ordered by Last Names); Contents; Part I Network Structure; An Empirical Study of the Effect of Noise Modelson Centrality Metrics; 1 Introduction; 2 Experimental Setup; 2.1 Test Suite of Networks; 2.2 Centrality Metrics; 2.3 Methodology; 3 Empirical Results; 3.1 Edge Addition; 3.2 Edge Deletion; 3.3 Edge Swap; 3.4 Edge XOR; 3.5 Summary of the Results; 4 Related Research; 5 Conclusion and Future Work; References; Emergence and Evolution of Hierarchical Structurein Complex Systems; 1 Introduction; 2 Lexis Background; 2.1 Lexis-DAG

Text of Note

2.2 The Lexis Optimization Problem2.3 Path Centrality and the Core of a Lexis-DAG; 2.4 Hourglass Score; 3 Evo-Lexis Framework and Metrics; 3.1 Incremental Design Algorithm; 3.2 Target Generation Models; 3.2.1 MRS Model; 3.2.2 MS Model; 3.2.3 M Model; 3.2.4 RND Model; 3.3 Key Metrics; 3.3.1 Cost Metrics; 3.3.2 Topological Metrics; 3.3.3 Target Diversity Metric; 4 Computational Results; 4.1 Parameter Values and Evolutionary Iteration; 4.2 Results; 4.2.1 Emergence of Low-Cost Hierarchies Due to Tinkering/Mutation and Selection

Text of Note

4.2.2 Low-Cost Design Resulting in Deeper Hierarchies and Reuse of More Complex Modules4.2.3 The Recombination Mechanism Creates Target Diversity; 4.2.4 Reuse of Complex Modules in the Core Set by Strong Selection; 4.2.5 Emergence of Hourglass Architecture Due to the Heavy Reuse of Complex Intermediate Modules in Models with Strong Selection; 4.2.6 Stability of the Core Set Due to Selection; 4.2.7 Fragility Caused by Stronger Selection; 5 Evolvability and the Space of Possible Targets; 6 Major Transitions; 7 Overhead of Incremental Design; 8 Discussion and Prior Work

Text of Note

4.6 Network Recovery4.7 Universal Resilience Curves [15]; 4.8 Insights and Conclusions; References; Part II Network Dynamics; Automatic Discovery of Families of Network Generative Processes; 1 Introduction; 2 Network Morphogenesis; 2.1 Reconstructing Processes; 2.1.1 Using Micro-Level Processes; 2.1.2 Using Macro-Level Structure; 2.2 Reconstructing Structure; 2.2.1 Using Processes; 2.2.2 Using Structure; 2.3 Combining Both: Evolutionary Models; 3 Symbolic Regression of Network Generators; 4 Families of Network Generators; 4.1 Protocol; 4.2 A Measure of Generator Dissimilarity; 4.3 Two-Dimensional Embedding and Families

Text of Note

8.1 Modularity and Hierarchy8.2 Hourglass Architecture; 8.3 Interplay of Design Adaptation and Evolution; 8.4 From Abstract Modeling to Specific Evolving Systems; 9 Conclusion; References; Evaluation of Cascading Infrastructure Failures and Optimal Recovery from a Network Science Perspective; 1 Introduction; 2 Risk and Resiliency; 2.1 Assessing Risk; 2.2 Gaps in the Risk Literature; 2.3 Moving Towards Resilience; 3 Network Science as a Tool; 4 Case Studies; 4.1 Studying Resilience Curves; 4.2 Data; 4.3 Limitations of the Data; 4.4 Network Analysis of IEEE Bus Test Case; 4.5 Network Robustness

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Text of Note

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.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9783030146832

Title

Dynamics on and of complex networks III.

International Standard Book Number

9783030146825

Big data.

Computer networks.

Machine learning.

Statistical physics.

Big data.

Computer networks.

Machine learning.

Statistical physics.

JHBC

JHBC

PSAF

SCI064000

Number

Edition

Class number

Book number

Date of Transaction

20200823083911.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y