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dschult
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I've got a partial review here... but I thought I would submit it rather than wait for the time to get through everything in the notebook. I hope it doesn't ramble too much. :)
This looks quite good. These comments and suggestions are intended to discuss areas that are maybe hard to describe. And there is a choice to be made between more detail and thus maybe too much information, versus less detail and thus maybe too little information. So, if anything here is too detailed or not detailed enough, adjust as you see fit.
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| To represent the transition probabilities, a transition matrix is constructed as shown above. It is a square matrix with its rows and columns corresponding to the states (web pages). Entry $ P_{i j} $ in matrix represents the transition probability of going from state $i$ to state $j$. | ||
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| The fundamental components of a Markov chain are the set of states, transition probabilities, and an initial state distribution. |
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These components are the "inputs" to the markov chain. Another important component is the output: the chance of being in each state in the long run.
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I believe the long-run probability of being in each state is the output of computing the stationary distribution of a Markov chain and not inherently a component of it. Plus, there exist periodic and reducible Markov chains that will not have a stable long run probability distribution.
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Yes -- I was using the word "component" as you do in the text. Not in the sense of a graph component, but in the sense of a part of the Markov chain process. Maybe the wording "The fundamental parts of a Markov chain...". Or with the wording I used, "The fundamental inputs to a Markov chain..."
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@navyagarwal I have fixed up the notebooks (heading levels and linting) so they pass our CI. You would need to pull down the changes |
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Yeah those are the ones :) but it misses one step (a git add content/) after the conversion to ipynb. I would suggest looking at the CI workflow step for linting https://github.com/networkx/nx-guides/blob/main/.github/workflows/notebooks.yml And as you can see it's not too intuitive to do this currently but we are stuck with this process for now 😅 but we really should do a better job of documenting everything here. |
dschult
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Here are some minor wording suggestions for the PageRank Notebook. It basically finishes the suggestions I started a few months ago. You can adapt/change/ignore any and all of the suggestions. Thanks for the nice pagerank description!
Co-authored-by: Dan Schult <dschult@colgate.edu>
Co-authored-by: Dan Schult <dschult@colgate.edu>
Here's the notebook for the PageRank algorithm.
I would love to get an initial review. (I am concerned that some portions might have become too complex)
(The function
my_draw_networkx_edge_labelsat the end of the notebook will be added to the main NetworkX repo.)