Computational Social Science and Complex Systems.

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 -- 1. 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 -- 5. Conclusions -- Introduction to market microstructure and heterogeneity of investors -- 1. Introduction -- 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 -- 1. Introduction -- 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 -- 1. Introduction -- 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 -- 1. Introduction -- 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 -- 1. Introduction -- 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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Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.