xiaofan wang [email protected] 2014 network science: an introduction
TRANSCRIPT
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- Xiaofan Wang [email protected] 2014 Network Science: An Introduction
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- Network Science Measuring Data Measuring Data Discovering Property Modeling Network Modeling Network Analysis Behavior Analysis Behavior Design Performance
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- Collect enough information so that you can Describe (correctly) Quantify (properly) Formulate (mathematically ) Predict (reasonably) Control (powerfully)
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- Communication Transportation Power Grid Social Economical NetworkSpreading Biological
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- Virus Fasion Behavior Rumor Belief Spreading on Social Networks (video) Spreading on Social Networks (video) Opinion
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- Theoretical Science Graph Theory Game Theory Statistical Physics Computer Science Applied Science Communication Science Power Engineering Life Science Social Science
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- For every technology, the first ten years is the development, and the second ten years is when the market follows.
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- Science, in general, is a lot better at breaking complex things into tiny parts than it is at figuring out how tiny parts turn into complex things.
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- Interdisciplinary nature Data-driven nature Quantitative nature Computational nature
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- Interdisciplinary nature Data-driven nature Quantitative nature Computational nature
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- Graph Theory Spectral Graph Theory Markov Chain Theory Fan Chung UCSD
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- A.-L. Barabsi Northeastern Mark Newman Michigan Mean-Field Theory Phase Transition Percolation Theory
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- Jon Kleinberg Cornell
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- Liu Y Y, Slotine J J, Barabsi A L. Nature, 2011, 473(7346): 167-173.
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- Sinan Aral MIT
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- Lev Muchnik, Sinan Aral, and Sean J. Taylor, Social Influence Bias: A Randomized Experiment, Science 9 August 2013: 341 (6146), 647-651. 101281 32%
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- Unweighted Undirected Weighted Undirected Unweighted Directed Weighted Directed
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- 101 144 101 242
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- No Multi-edge No Self-edge
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- 99.9% 32% Facebook Friendship network: Facebook Love network:
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- Many networks have a unique giant component
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- If you have 2 large-components each occupying roughly 1/2 of the graph. How many random edges do you need to add so that the probability that the two components join into one giant component is greater than 0.9? (a) 1-5 edge additions (b) 6-10 edge additions (c) 11-15 edge additions (d) 16-20 edge additions
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- (Giant weakly connected component, GWCC) Bow-tie structure (SCC) (IN) (OUT) (TENDRILS)
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- (Giant weakly connected component, GWCC) Bow-tie structure (SCC) (IN) (OUT) (TENDRILS)
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- Average degree
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- Out-Degree In-Degree
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- Whole net out-degree equals to in-degree Single node out-degree may not equal to in-degree
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- = 349 54785
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- Globally coupled net k=N-1, M~O(N 2 ) Practical net