💯 to needing a better metric regarding npubs.
Maybe PageRank. Except … that won’t work. PageRank is an extrinsic metric. Follower count is another extrinsic metric. I want an intrinsic metric.
Example of an extrinsic metric: fame. It’s a relationship between the user and the community.
Examples of intrinsic metrics: height, weight, intelligence. Those things are independent of the community.
PageRank is an extrinsic metric, kinda like fame. We need to modify it so it’s an intrinsic metric, more like intelligence.
GrapeRank is a modification of PageRank that converts it into an intrinsic metric.
Suppose I want to know the height of user1. Users 2, 3, and 4 estimate 5’11, 6’1, and 6’0, and communicate those results to me. What do I do with those numbers?
One idea is to add them together. User1 is … 18 ‘ tall. Obviously that’s the wrong metric. This is the PageRank approach.
The right metric: weighted average, which comes to 6 feet, give or take, depending on which of users 2,3,4 I trust the most.
GrapeRank is a weighted average. Simple as that.
GrapeRank is a nonlinear equation, as opposed to PageRank, which is linear. But who cares? Use the easy algo and get the useless metric (18 feet), or the slightly harder algo and get a useful answer that estimates objective reality (~ 6 feet).
Trynta write this out in long form today. 🤓
straycat / david
npub1u5…rldq3
2024-08-29 13:34:49
in reply to nevent1q…tpxj
Author Public Key
npub1u5njm6g5h5cpw4wy8xugu62e5s7f6fnysv0sj0z3a8rengt2zqhsxrldq3Published at
2024-08-29 13:34:49Event JSON
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"content": "💯 to needing a better metric regarding npubs.\n\nMaybe PageRank. Except … that won’t work. PageRank is an extrinsic metric. Follower count is another extrinsic metric. I want an intrinsic metric.\n\nExample of an extrinsic metric: fame. It’s a relationship between the user and the community.\n\nExamples of intrinsic metrics: height, weight, intelligence. Those things are independent of the community.\n\nPageRank is an extrinsic metric, kinda like fame. We need to modify it so it’s an intrinsic metric, more like intelligence.\n\nGrapeRank is a modification of PageRank that converts it into an intrinsic metric.\n\nSuppose I want to know the height of user1. Users 2, 3, and 4 estimate 5’11, 6’1, and 6’0, and communicate those results to me. What do I do with those numbers?\n\nOne idea is to add them together. User1 is … 18 ‘ tall. Obviously that’s the wrong metric. This is the PageRank approach.\n\nThe right metric: weighted average, which comes to 6 feet, give or take, depending on which of users 2,3,4 I trust the most. \n\nGrapeRank is a weighted average. Simple as that.\n\nGrapeRank is a nonlinear equation, as opposed to PageRank, which is linear. But who cares? Use the easy algo and get the useless metric (18 feet), or the slightly harder algo and get a useful answer that estimates objective reality (~ 6 feet).\n\nTrynta write this out in long form today. 🤓",
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