CSSS 2010 Santa Fe-Readings: Difference between revisions
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===Jure Leskovec=== | ===Jure Leskovec=== | ||
1) -- models and link prediction in large social networks: | 1) -- models and link prediction in large social networks: | ||
[http://i.stanford.edu/~jure/pub/csss/kronecker-CSSS-jun10.pdf Lecture Slides] | |||
* [http://cs.stanford.edu/people/jure/pubs/kronecker-jmlr10.pdf Kronecker Graphs] | * [http://cs.stanford.edu/people/jure/pubs/kronecker-jmlr10.pdf Kronecker Graphs] | ||
* [http://cs.stanford.edu/people/jure/pubs/microEvol-kdd08.pdf Microscopic Evolution of Social Networks ] | * [http://cs.stanford.edu/people/jure/pubs/microEvol-kdd08.pdf Microscopic Evolution of Social Networks ] | ||
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2) -- tracking information diffusion, finding influencers and | 2) -- tracking information diffusion, finding influencers and | ||
detecting disease outbreaks in networks | detecting disease outbreaks in networks | ||
[http://i.stanford.edu/~jure/pub/csss/memetracker-CSSS-jun10.pdf Lecture Slides] | |||
* [http://cs.stanford.edu/people/jure/pubs/quotes-kdd09.pdf Meme Tracking & News Cycle Dynamics] | * [http://cs.stanford.edu/people/jure/pubs/quotes-kdd09.pdf Meme Tracking & News Cycle Dynamics] | ||
* [http://www.cs.cornell.edu/home/kleinber/kdd03-inf.pdf Maximizing the Spread of Influence through a Social Network] | * [http://www.cs.cornell.edu/home/kleinber/kdd03-inf.pdf Maximizing the Spread of Influence through a Social Network] | ||
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3) -- models of networks with positive and negative ties | 3) -- models of networks with positive and negative ties | ||
[http://i.stanford.edu/~jure/pub/csss/signedNets-CSSS-jun10.pdf Lecture Slides] | |||
* [http://www.iw3c2.org/WWW2004/docs/1p403.pdf Trust & Distrust] | * [http://www.iw3c2.org/WWW2004/docs/1p403.pdf Trust & Distrust] | ||
* [http://cs.stanford.edu/people/jure/pubs/triads-chi10.pdf Signed Networks & Social Media:] | * [http://cs.stanford.edu/people/jure/pubs/triads-chi10.pdf Signed Networks & Social Media:] | ||
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* [http://www.how-why.com/cgi-bin/cyberprof/os.exe?home=&document=http://www.how-why.com/ph510/Notes/index.html PHYCS510 UIUC notes] | * [http://www.how-why.com/cgi-bin/cyberprof/os.exe?home=&document=http://www.how-why.com/ph510/Notes/index.html PHYCS510 UIUC notes] | ||
---- | ---- | ||
===David Krakauer=== | |||
* [[Media:Krakauer1.pdf | Lecture 1]] | |||
* [[Media:Krakauer2.pdf | Lecture 2]] | |||
---- | |||
===Scott Pauls=== | ===Scott Pauls=== | ||
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* [http://www.math.dartmouth.edu/~pauls/PDM_Toolkit.zip Code package]. | * [http://www.math.dartmouth.edu/~pauls/PDM_Toolkit.zip Code package]. | ||
---- | |||
===Murray Gell-Mann=== | ===Murray Gell-Mann=== | ||
* [[Media:MGM_134.pdf | Lecture]] | * [[Media:MGM_134.pdf | Effective Complexity]] | ||
* [[Media:Northwestern.ppt | Lecture]] | |||
Latest revision as of 03:53, 28 April 2011
| CSSS Santa Fe 2010 |
Please review readings before lectures. Supplemental material may be posted as well.
Background Readings
Please see the Background Readings page for assignments.
Dan Rockmore
Dan's Intro Lecture
Draft intro to stat learning primer
Tanmoy Bhattacharya
Inference in Historical Processes
Liz Bradley
- Numerical Solution of Differential Equations
- Time Series Analysis
- File:BradleySlides2010.pdf
- File:BradleySyllabus2010.pdf
Tom Carter
Here is a link to a page with various background readings -- I'll be talking about some of this material, watch the wiki for days/times
Iain Couzin
- Complexity. An Informative Itinerary
- Collective Cognition in Animal Groups
- Collective Motion and Cannibalism in Locust Migratory Bands
- Collective Minds
- Effective Leadership and Decision-Making in Animal Groups on the Move
Lecture Slides
Iain's Lecture Slides
Simon DeDeo
For the two lectures on "Physics of Reasoning" -- all the papers, as well as scans of the notes, are available at this page. Feel free to e-mail me at simon@santafe.edu
(simon's blog page with more tasty links is at http://tuvalu.santafe.edu/~simon/ )
Owen Densmore
Steve Guerin and I will present modeling with NetLogo. It will have two parts, one a self-paced wiki tutorial, the second a set of real world examples of modeling we've used professionally.
Read the first page of the tutorial. Then (attempt!) to download, install, and look at the Model Library.
Peter Dodds
Nathan Eagle
Doug Erwin
Duncan Foley
- Labor, Capital and Land in the New Economy
- Economic Complexity and Dynamics in Interactive Systems
- Hyman Minsky and the Dilemmas of Contemporary Economic Method
- Lecture 1
- Lecture 2
Laura Fortunato
Greg Leibon
- Lecture 1 notes
- Lecture 2 note
- Lecture 3 note
- Lecture 1 slides
- Lecture 2 slides
- Lecture 3 slides
- Markov Chains In A Shoebox
- Conformal Geometry of Markov Chains
John Harte
Jure Leskovec
1) -- models and link prediction in large social networks: Lecture Slides
2) -- tracking information diffusion, finding influencers and detecting disease outbreaks in networks Lecture Slides
- Meme Tracking & News Cycle Dynamics
- Maximizing the Spread of Influence through a Social Network
- Outbreak Detection in Social Networks
- 2004 Election Political Blogger Networks
3) -- models of networks with positive and negative ties Lecture Slides
- Trust & Distrust
- Signed Networks & Social Media:
- Predicting Postive & Negative Links in Online Social Networks
- Governance in Social Media
Cosma Shalizi
- Quantifying Self-Organization with Optimal Predictors
- Methods and Techniques of Complex Systems Science: An Overview
- Blind Construction of Optimal Nonlinear Recursive Predictors for Discrete Sequences
- Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems
- Computational Mechanics: Pattern and Prediction, Structure and Simplicity
- The Computational Structure of Spike Trains
- Discovering Functional Communities in Dynamical Networks
- Measuring Shared Information and Coordinated Activity in Neuronal Networks
- Optimal Nonlinear Prediction of Random Fields on Networks
Data mining and statistical learning lecture notes
Chaos, complexity and inference lecture notes
CSSS lecture slides:
Bill Croft, Ian Maddieson, Eric Smith
Alfred Hübler
Username/Pass: guest/guest
David Krakauer
Scott Pauls
- Lecture on using the PDM on voting data (.pptx).
- Lecture(.PPT Version)
- Google Docs version for us non-powerpoint people.
- Code package.