Notes to add to README when there is time

[alexpghayes]

README beyond this point is really just scratch for myself

Sink nodes and unreachable nodes

citation_graph <- sample_pa(100)

citation_tracker <- appr(citation_graph, seeds = "5")
citation_tracker

Why should I use aPPR?

• curious about nodes important to the community around a particular user who you wouldn't find without algorithmic help

• 1 hop network is too small, 2-3 hop networks are too large (recall diameter of twitter graph is 3.7!!!)

• want to study a particular community but don't know exactly which accounts to investigate, but you do have a good idea of one or two important accounts in that community

aPPR calculates an approximation

comment on p = 0 versus p != 0

Advice on choosing epsilon

Number of unique visits as a function of epsilon, wait times, runtime proportion to 1 / (alpha * epsilon), etc, etc

speaking strictly in terms of the p != 0 nodes

1e-4 and 1e-5: finishes quickly, neighbors with high degree get visited
1e-6: visits most of 1-hop neighborhood. finishes in several hours for accounts who follow thousands of people with ~10 tokens.
1e-7: visits beyond the 1-hop neighbor by ???. takes a couple days to run with ~10 tokens.
1e-8: visits a lot beyond the 1-hop neighbor, presumably the important people in the 2-hop neighbor, ???

the most disparate a users interests, and the less connected their neighborhood, the longer it will take to run aPPR

Limitations

• Connected graph assumption, what results look like when we violate this assumption
• Sampling is one node at a time

Speed ideas

compute is not an issue relative to actually getting data

Compute time ~ access from Ram time << access from disk time << access from network time.

Make requests to API in bulk, memoize everything, cache / write to disk in a separate process?

General pattern: cache on disk, and also in RAM

Working with Tracker objects

See ?Tracker for details.