Yep, it's stupid simple: https://coracle.social/help/web-of-trust
Also see: https://github.com/coracle-social/welshman/blob/master/packages/app/src/wot.ts
nostr:npub1jlrs53pkdfjnts29kveljul2sm0actt6n8dxrrzqcersttvcuv3qdjynqn do you have an documentation on how your WoT score works in coracle? I've built an easy way for loading the WoT in applesauce and Id like to use the same scoring system as you've used in coracle in noStrudel
https://hzrd149.github.io/applesauce/examples/#loader/social-graph
Discussion
yaaaaaaas.
this conversation makes me excited.
What are the chances of integrating nostr:nprofile1qythwumn8ghj7un9d3shjtnwdaehgu3wvfskuep0qy2hwumn8ghj7un9d3shjtnyv9kh2uewd9hj7qghwaehxw309aex2mrp0yh8qunfd4skctnwv46z7qpqkpt95rv4q3mcz8e4lamwtxq7men6jprf49l7asfac9lnv2gda0lqyqm7e2 DVMs for spam filtering at the client level? I think it's the best thought out solution. Maybe nostr:nprofile1qy2hwumn8ghj7un9d3shjtnyv9kh2uewd9hj7qghwaehxw309aex2mrp0yhxummnw3ezucnpdejz7qghwaehxw309aex2mrp0yh8qunfd4skctnwv46z7qgkwaehxw309ash2arg9ehx7um5wgcjucm0d5hsqg8ks058qd0h4485fc9e3naaj5m7zez44ykd8r80cn9nrkm42l677ghnejy3 can chime in
nostr:nprofile1qy88wumn8ghj7mn0wvhxcmmv9uq3zamnwvaz7tmwdaehgu3wwa5kuef0qqszv6q4uryjzr06xfxxew34wwc5hmjfmfpqn229d72gfegsdn2q3fgfjtyk0 and nostr:nprofile1qyvhwumn8ghj76rzwghxxmmjv93kcefwwdhkx6tpdshszrnhwden5te0dehhxtnvdakz7qpqjlrs53pkdfjnts29kveljul2sm0actt6n8dxrrzqcersttvcuv3qy09qsr, the WoT coracle has a few issues, that will become more and more apparent as nostr grows:
1. each follows/mute count 1. This doesn't consider that there are people that follow 5k others, while most are more picky, nor considers the reputation of the one following. These nuances are reflected by the pagerank formula for instance https://en.wikipedia.org/wiki/PageRank.
The contribute of one's follows are proportional to its pagerank and inversely proportional to the size of the follow-list. This is true both for global or personalized pagerank.
2. a users has only information on the two-hop network around him. This consists of an average of ~50k people, which is fine for now because the network is small, but won't scale when nostr is at 10M+ users.
Vertex abstracts away all of these complexities, and gives simple to use end-points for many common operations like:
- sorting replies, comments, zaps...
- verify reputation of a target npub
- get recommendations
I am more than happy to have a chat about how give access to these tools guys, just let me know
Great chime in sir :fire:
nostr:npub1ye5ptcxfyyxl5vjvdjar2ua3f0hynkjzpx552mu5snj3qmx5pzjscpknpr just convinced to stop using the Follow concept entirely, lol.
Very curious how pageranking will be done in a more Community centered layer of Nostr, with atomic Label/Badge events.
honestly if a community is small enough, Pagerank is not needed because all npubs have been invited.
For open communities, then Pagerank is useful for helping the moderator know which one to kick out automatically
That's true **within** a community.
The questions come when you want to use the Communitues someone is active on as data to derive a notion of **his network** from.
As in:
When he browse to the page of a product / a new community / a podcast show / ....
Do you show the profiles on his network that are part of his network that use that product, are active in that community and are listening to that podcast?
If yes, then you need a way to define that network.
Or for the main Vertex use case too:
For knowing if you are the real Pip, you are now looking at the Web of Follows.
What changes mathematically when we start looking at a Web of Communities?
A web that works with:
- Badges: awarded by Communities, (optionally also accepted by profiles)
- payments (pay to poast, pay for badge, zap the communikey, ....)
- atomic events for reporting and black "listing"
It really depends on the usecase, there is no silver bullet.
For example, if you are looking at a product, you could fetch all reviews for that product, and drop reviews from npubs whose pagerank is lower than a threshold. Or you could weight that review using the pagerank (influencers have higher influence, not necessarily a good idea).
Or maybe you can show the average vote of the members of the community, which is more contained. All tradeoffs.
Pagerank can be run on any graph, so one could built it by looking at who replied/zapped/liked to whom, in a given setting.
Also, not all signal should be condensed into a single rank, multiple trust signals should be provided imo. Number of followers, ranks, nip05, badges, all give some clues that your brain can interpret.
I agree with your first point, valuing each follow/mute should take into account how many users the user is following. I think ill try to learn the math behind page rank and see if it works any better for me
For 2. I agree this does not scale but I'm trying to find a simple enough solution that a pure (no server) nostr client can implement that will be the most effective. Once I figure out how to implement the "simple" self sovereign method I would love to see how this could be off loaded to a third party server (or even a personal server on a Umbrel)
But for my use case in noStrudel I need the "WoT" to do two things:
1. Sort pubkeys by "rank" (find the real hodlbod test)
2. Filter out new, on-off spam accounts
The effectiveness of these things will vary based on if the user is using a locally calculated WoT or a remote service, but I think thats fine
I agree, local first, then pagerank as progressive enhancement
personalized Pagerank is the best "local", that is as wide as possible, but still relevant.
but yeah it's hard to compute locally.
Would it change if these ranks where computed server-side, but you can do client-side verification that prevent the service provider from lying?
I have a few ideas in mind.
No, because I still want wot numbers if the server goes down
okay, so it's more a matter of uptime rather than trust, am I right?
I am thinking about running a second relay so that if the first goes down the second can cover.
isn't it possible to store a cache locally and update it once every unit of time? This would radically lower the uptime requirement because the server now needs only to be online once every hour/day/week...
It's both uptime and trust, a local fallback will always be necessary. I could run a cache, but it would also be a third party service with questionable uptime, it's just good to have something built into the software itself because it's such an important core function
I am missing something sir. I assume that Coracle stores locally the WoT calculations it computes client-side. Is it true?
If so, why can't it store the ranks provided by Vertex in the same way?
Because the problem is cold start. Someone (new or returning) opening the app for the first time won't have a cache. The cache gets invalidated pretty often too due to limits with web storage
yeah but this is true for both your current WoT and the Vertex ranks, no?
The way I imagine it is:
- a user main feed is based on its follow-list, which is pulled from relays
- as the user starts clicking around (search, reading replies...), this calls a service provider like Vertex, and the ranks are stored for later use.
So it would progressively load ranks as needed, not ahead of time.
Believe me I am not trying to push anything, just trying to genuinely understand.
Yes, but client-calculated wot can be pulled from relays. IOW, vertex is a centralizing factor. It's benign, but still centralizing.
The issue with client-calculated is low quality and suboptimal UX. The problem I reported with profile feeds taking long to show up in Coracle appears to be related, as you see the notes spinner while the "follower" list gets populated. Correct me if I'm wrong.
Having a fallback mechanism sounds like the best of both worlds.
No, that doesn't sound right, wot is calculated concurrently, it shouldn't be holding up feeds (unless too many requests are being sent to a single relay).
> Having a fallback mechanism sounds like the best of both worlds.
Yes, that's what I'm saying
Yes, that'd work for Community Apps.
As the communities' job and effect is that they do most of the spam prevention + user validation.
Page rank is more rarely needed for the individual user and it's fine if servers go down sometimes.
I can fall back on a light local "user network" calculation by using the Communities and Contacts and going max two level deep (ideally just one even, which'd be fine).
yeah, I think one level should be stored locally, as it's very easy and useful, and then server for everything else.
I'm not too worried about the server lying, since ideally users could choose their provider or maybe run their own.
The way I see it there are two uses for pagerank or "WoT"
1. The client wants to hide content that is outside of the users social network (subjective distance or score)
I'm not sure how this could work without the server sending the whole graph (probably Mb of data) or the client making API requests to check each pubkey (not great for privacy)
For this case I think the simple kind:3 WoT is good enough, at least for now
2. The client wants to sort events or pubkeys by "reputation" or score
Vertex already does this well for user search ( query -> pubkey[] ) but it may be useful to have some kind of generic sorting method for events to allow a client to sort replies ( again probably bad for privacy )
I tend to agree with nostr:npub1jlrs53pkdfjnts29kveljul2sm0actt6n8dxrrzqcersttvcuv3qdjynqn. I want my client to work without a dependency on a server. but I'd also like to integrate with some kind of server to improve performance or to get better sorting / filtering results
Random thought. maybe bloom filters could be space efficient way to handle the filtering use case? I don't know much about them but AI says they are used to prove an ID is within a set
I think for 1. and 2. RankProfiles is a solid approach
https://vertexlab.io/docs/services/rank-profiles/
Let's go through the case with replies:
- user clicks on a note
- client loads all replies for that note
- extract the pubkeys of these replies
- client asks Vertex for the ranks of up-to 1000 pubkeys in a single call, with the user's preferred algorithm
- client hides replies from npubs with rank lower than the user's preferred threshold
- client ranks the other replies by their rank (or a custom formula like rank * zap amount).
It's an interesting idea but I don't see where exactly in the client you would need it.
- for the feed one uses follows
- for the replies, because on the relatively low number one can use the ranks directly (giving the user the ability to set a threshold).
Bloom filters are superior to RankProfiles when you are dealing with +10k pubkeys (not in the follow-list of the user) I think.
Am I missing something?
I think your right, the only other one off use case I could think of where the client would need a filter instead of sorting would be if they wanted hide zaps / reactions or browser "global" on a relay. but none of those are things I'm trying to implement :)
Do u mean zaps and reactions in the users notifications?
I think that too can be done using RankProfiles. It's unlikely one has thousands of notifications, but not impossible if its targeted by an attacker