For today you should:
1) Finish reading Chapter 3 and prepare reading questions below.
2) Work on Chapter 3 exercises.
3) Read the Watts and Strogatz paper.
1) Analysis of Graph.py.
2) Case studies.
3) Chapter 3 reading questions.
For next time you should:
1) Check the performance of your Graph.py
2) Read Chapter 4 and answer the reading questions below.
3) Read the paper by Barabasi and Albert, below.
A good case study has 3-4 of the following elements:
1) Includes a motivating question and an interesting model.
2) Uses a new Python feature or a cool module.
3) Uses a non-trivial algorithm.
4) Raises an interesting philosophical question.
5) Lends itself to further exploration.
In order to start the hunt, we will move through the book quickly, about one chapter per meeting. Done by Oct 18 or 21, which is the half-way point. Then we will do some idea-generation and team formation.
Work on exercises in the background.
1) State Zipf's law in simple words.
2) Draw in the figure that's missing from page 35.
3) How can you test a dataset to see if its distribution is Pareto (at least approximately, at least in the tail)?
4) In the BA model, what does the distribution of node degrees look like?
5) What aspect of the model yields this behavior?
6) What is the relationship between Zipf's law, power laws, and Pareto distributions?
7) In what way is an explanatory model like an argument by analogy?
8) Does the WS model "explain" the small world phenomenon? What about the BA model?
1) What are the mechanisms that cause BA graphs to have "complex topology"?
2) What is the point of Figure 1? What is the take-home message?
3) What is the point of Figure 2?
4) What claim do the authors make to connect Figures 1 and 2?
5) In the abstract, what do the authors mean by "robust"?