Add jupyter notebook with common neighbours metric on football dataset #342
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FYI, Github will actually render a |
devoxel
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Feb 12, 2019
devoxel
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Feb 13, 2019
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Connects to #304.
This PR implements the common neighbours metric, and runs it on one of the Pajek datasets used in the paper in #313. Included is loading the dataset, computing the similarity matrix, and computing the AUC (area under receiver operating characteristic curve), which is a measure of true positives vs. false positives. The formula to calculate the AUC (based on n1, n2, n3) comes from the same paper mentioned above.
Added in a new
research/directory, which we can use for a lot of non-prod code in future I guess - experiments, explorations, etc.What will be required in the actual implementation of this metric in Rabble is a microservice including the computation of a similarity matrix, with some extra bits for eg. updating the matrix regularly (as our follow graph changes), an API for actually getting recommendations from the similarity matrix (ie. return some number of
js whereS[i][j]is maximal for some giveni), and maybe a slightly different accuracy measurement, depending on how easy it is to apply AUC to our own database. Some of the code here might be replicated there later, IDK for sure.Our results in this notebook:
vs. the paper's results with this metric on the same dataset:
Compare the
Averagerow of our output with the[n1 n2 n3 AUC]in theAveragecolumn from the paper.