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Alessandro Vespignani Science, Vol. 325 24 July 2009 (Prepared by Hasan T Karaoglu)

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Presentation on theme: "Alessandro Vespignani Science, Vol. 325 24 July 2009 (Prepared by Hasan T Karaoglu)"— Presentation transcript:

1 Alessandro Vespignani Science, Vol July 2009 (Prepared by Hasan T Karaoglu)

2  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

3  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?

4  Quantitative Prediction of Spatio-Temporal Patterns of Pandemics?  Effects of connecting billions of people from China and India using Internet?  Internet Stability and Growth?

5  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

6  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.

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8 Airline Traffic

9 Commuting Traffic

10  What it brings? ◦ Dynamics of Epidemics ◦ Evolution of Languages and Dialects ◦ Bio-invasion ◦ Foraging for Information

11  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

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13  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

14  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

15  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.

16  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?

17  Total Disappointment: Twitter  Reddit, Diggit  Spinn3r?  Emotion Extraction?


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