Presentation on theme: "Alessandro Vespignani Science, Vol. 325 24 July 2009 (Prepared by Hasan T Karaoglu)"— Presentation transcript:
Alessandro Vespignani Science, Vol July 2009 (Prepared by Hasan T Karaoglu)
What is the Question? Possible Answers Reality Mining (and Proxy Networks) Network Thinking Highlights Some Applications and Caveats Additional Slides ◦ Couple of Examples ◦ Progress Report
Techno-social systems ◦ Large-scale physical infrastructure (Power Grids, Transportation Systems, Internet...) ◦ Embedded in Dense Web of Communication ◦ Led by Human While we can predict weather conditions successfully, why we couldn’t achieve same success in predicting social systems’ behavior?
Quantitative Prediction of Spatio-Temporal Patterns of Pandemics? Effects of connecting billions of people from China and India using Internet? Internet Stability and Growth?
To predict: ◦ Real world patterns discovered in data ◦ Forming models based on patterns Not Enough Data ◦ Centuries of Weather Condition Records ◦ Not enough data for social systems till lately Mobility, Adaption of Certain Behavior, Risk Perception Fundamentals of System Model ◦ Physical Laws governing fluids and gases ◦ We need better understanding of social interaction
Level of Information Flow ◦ Not only due to Computational Power ◦ Involvement of machines into our life Machine Sensed Data Related to Our Lives Human Mobility ◦ and ◦ Cell Phones, PDAs, Bluetooth, WiFi, GPS, Sensors ◦ Mobile Phone Track of 100K people over 6 months.
What it brings? ◦ Dynamics of Epidemics ◦ Evolution of Languages and Dialects ◦ Bio-invasion ◦ Foraging for Information
Real World Networks are mostly “self- organized” ◦ Heavy Tailed and Skewed ◦ Heterogenity ◦ Similar Behavior in Different Granularities Complexity of Techno-social Systems ◦ “Network Mindset” Ex: ◦ 14 th Century Plague Epidemic : Spatial Diffusion ◦ SARS : Commercial Air Travel
Large Scale Systems: ◦ Don’t exhibit engineered or planned behavior ◦ Ex: Commuting Networks Final System behavior is result of: ◦ Dynamics of all scales ◦ Events take place at different timelines Bottom-up Approach ◦ Behaviors of Individuals shape the Large-Scale System Behavior ◦ Flocks of Birds, Internet Topology
Flocking ◦ Separation (don’t crowd your neighbors) ◦ Alignment (position yourself in the middle) ◦ Cohesion (follow your neighbors) Internet Topology ◦ Wealth Based Topology Generator ◦ Establish links as you have money ◦ Go bankrupt when you broke ◦ Randomly choose whom to connect Maxwell-Boltzman Distribution
Social Atoms to Social Aggregate The shift from the study of a small number of elements to the study of the behavior of large-scale aggregates is equivalent to the shift from atomic and molecular physics to the physics of matter.
System Modeling ◦ TRANSIM, EPISIM Counter-Intuitive Ideas ◦ Avoiding Cascading Failure ◦ Preventing Further Damage, Ex: Wild Fire, Immunization Limits ◦ Steady State Behavior (Catastrophe ?) ◦ Social Adaptive Behavior (Self fulfilling Prophecy) ◦ Ethical Issues?