Details

Subject(s)

• Social sciences—Data processing—Congresses [Browse]

Editor

• Kertész, János, 1950- [Browse]
• Mantegna, Rosario N. (Rosario Nunzio), 1960- [Browse]

Contributor

• Micciché, Salvo [Browse]

Series

• International School of Physics "Enrico Fermi." Proceedings of the International School of Physics "Enrico Fermi" ; Course 203. [More in this series]
• Proceedings of the International School of Physics "Enrico Fermi" ; Course 203

Source of description

Description based on print version record.

Contents

• Intro
• Title Page
• Contents
• Preface
• Course group shot
• Virtual social science
• 1. Introduction
• 1.1. What is social science?
• 1.1.1. Social systems are continuously restructuring networks
• 1.2. Social systems are complex systems
• 1.2.1. What is co-evolution?
• 2. A virtual society
• 2.1. The universe: the Pardus game
• 2.1.1. The census of avatars
• 2.1.2. The structure of the universe
• 2.1.3. Trade and economy
• 2.1.4. Communication
• 2.1.5. Friends and enemies
• 2.1.6. Performance measures of players - "states
• 2.1.7. Alliances
• 3. How do people interact?
• 3.1. Testing a classic sociological hypothesis of social interaction: weak ties
• 3.1.1. How strong do people interact? - Kepler's law
• 3.2. Forces between avatars - Newton's law for social interactions?
• 4. How do people organize?
• 4.1. Dynamics of the "atoms of society": triadic closure
• 4.1.1. Testing triadic closure - the triad-significance profile
• 4.2. Taking triadic closure seriously - understandingsocial multilayer network structure
• 4.2.1. Characteristic exponents
• 4.3. Degree distributions for negative ties are power laws - positive are not
• 4.4. Social balance
• 4.4.1. Origin of social balance
• 4.5. Avatars organize in multiples of four
• 4.5.1. Dunbar numbers
• 4.6. The behavioral code
• 4.6.1. Two ways of seeing the same data
• 4.6.2. Behavioral code and predicting behavior
• 4.6.3. Worldlines of players
• 4.6.4. Zipf's law in the human behavioral code
• 4.7. Network-network interactions
• 5. Gender differences
• 5.1. Gender differences in networking
• 5.1.1. Gender differences in network topology
• 5.1.2. Gender differences in temporal behavior
• 6. Mobility - how avatars move in their universe
• 6.1. Jump- and waiting time distributions
• 6.2. Long-term memory and mobility
• 7. The wealth of virtual nations.
• 7.1. More on the Pardus economy
• 7.2. Wealth
• 7.3. Inequality
• 7.4. Behavioral factors for wealth
• 7.4.1. Influence of activity on wealth
• 7.4.2. Influence of achievement factors on wealth
• 7.4.3. Wealth depends on how social you are
• 7.5. Wealth and position in the multilayer network
• 8. Towards a new social science?
• Measuring social and political phenomena on the web
• 1. Background and motivation
• 2. Measuring gender inequality on Wikipedia
• 3. Modeling minorities in social networks
• 4. Measuring voting power and behavior in liquid democracy
• 5. Conclusions
• Science of success: An introduction
• 2. Performance and success
• 2.1. Performance drives success
• 2.2. Perfomance is bounded
• 3. Success as a collective phenomenon
• 3.1. Success or recognition is unbounded
• 3.2. Success breeds success
• 3.3. Quality times previous success determines future success
• 4. Science of science
• 4.1. Quantifying long-term scientific impact
• 4.2. The Q-model
• 4.3. Credit is based on perception, not performance
• Introduction to market microstructure and heterogeneity of investors
• 2. A gentle introduction to limit order books
• 3. Market impact and order flow
• 3.1. Order flow
• 3.1.1. Origin of long memory
• 3.1.2. Heterogeneity of investors and long memory
• 3.2. Market impact
• 3.3. Impact of metaorders and square root law
• 3.3.1. Cross-impact
• 3.3.2. Co-impact
• 4. Heterogeneity in time scales
• 5. Conclusions and outlook
• A primer on statistically validated networks
• 2. Disparity filter
• 3. Multiple hypothesis test correction
• 4. Statistically validated networks
• 5. Examples of applications of statistically validated networks
• 6. Community detection in statistically validated networks.
• 7. Software for the computation and analysis of statistically validated networks
• 8. Conclusions
• Temporal networks: Characterization, motifs and spreading
• 2. Definition and characterization of temporal networks
• 2.1. Definition and representation
• 2.2. Characterization
• 3. Motifs in temporal networks
• 3.1. Time-evolution of static motifs
• 3.2. Mobility motifs
• 3.3. Temporal motifs
• 4. Spreading on temporal networks
• 5. Outlook
• Temporal networks of face-to-face interactions
• 2. Data, representations of data and structures
• 2.1. Statistics
• 2.2. Aggregated networks
• 2.3. Contact matrices and contact matrices of distributions
• 2.4. Structures
• 3. Models
• 4. Processes on temporal networks
• 5. Using data
• 5.1. Which representation to use
• 5.2. Designing and testing interventions
• 5.3. Incomplete datasets
• 6. Conclusion
• Introduction to modeling disease spread in space
• 1. Spatial spread of infectious disease epidemics
• 2. Spatially structured populations and metapopulation approach
• 2.1. Patches and coupling
• 2.2. Relevant spatial effects
• 3. The stochastic discrete metapopulation scheme
• 3.1. Effective approach
• 3.2. Mechanistic approach
• 4. Local vs. global invasion
• 4.1. Local epidemic threshold
• 4.2. Global invasion threshold
• 5. Going beyond basic assumptions
• 6. Conclusions
• Spatio-temporal infrastructure networks
• 2. Resilience properties of single networks
• 2.1. Traffic
• 2.2. Physiology
• 2.3. Climate
• 2.4. Recovery
• 3. Resilience of interdependent networks
• 3.1. Methods for reducing cascades
• 3.2. Networks of networks
• 3.3. Recovery of interdependent networks
• 3.4. Spatially embedded interdependent networks
• 3.5. Localized attack
• 3.6. Summary and further reading
• List of participants.

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ISBN

1-64368-037-4

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