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The distance weight is the importance that the social graph distance has over the final score. In this case, it is almost half of the weight, and the other validators make up the rest of the score. There is more information in the article we wrote and on the about page: https://www.relatr.xyz/about , or just ask if you have any other doubt!

Yes, just that. We are experimenting with the validators. Do you think that is a meaningful metric?

I like mutual follows as a metric. I think it's one of the more important ones when you're looking at a static graph of users following users, which I'm assuming you have in some form but you can correct me if I'm wrong. But I was wondering why it was 1.0, rather than something smaller because not all my follows/followers are mutual. And is the metric outgoing mutual (mutual follows / who I'm following) or incoming mutual (mutual follows / who's following me)? Maybe both could have some use, because "reciprocity" is a broad term.

Hey! Thanks for your feedback. Yes, definitely the Relatr approach is more left side of the curve than Vertex or any other PageRank algorithm. However, the approach that Relatr is following is still consistent since the base of the score calculation is based on the distance between the source pubkey and the target pubkey. In the default instance we are running, the source pubkey is gigi, so in order for an attacker to impersonate someone, it will need gigi to follow them or a gigi's contact to follow it. But the farther the distance, the bigger the decay, so fundamentally an attacker would need to do some social engineering to make it closer to the source pubkey to some degree, which is not easily gameable, especially if the social graph realizes it is an attacker/impersonator and publishes a mute list with the attacker pubkey in it. The other validators are easily gameable in some way, yes, like valid NIP-05, NIP-10002 events published, yes, that's easy to game, but the weight it has in the score calculation is little compared to social graph distance. There are also other validators that aren't easily gameable, like reciprocity (source and target follow each other), and we are thinking in adding some more like zaps and activity. In summary, this is an experiment, but so far it is working pretty well as tie breaker to determine which pubkey is more likely to be an impersonator or the "real" one, and ideally everyone runs an instance that sets their pubkey as source so the trust score comes from their perspective. On the other hand, yes, the other relevant Jack didn't appear because they weren't being discovered during the first profile metadata sync that Relatr does. This is a bug; it's still early days, but after searching explicitly for them, they now appear as they are added to the metadata DB. Also, when you look for Jack.

glad you solved the bug about the jack's. About attack's resistance, distance-based WoT is inherently weaker than Pagerank becaus eyou just need one compromised (or lazy) key at distance n to include an impostor at distance n+1. Instead Pagerank considers distance as well as flow, which is the number of paths that connect a target to a source. 1 path is gonna give much less rank compared to 100 distincts paths, while the distance is going to be the same

That's for sure; the key here is still that the trust is computed from a specific source pubkey. So, if there is a more relevant pubkey in the social graph than the impersonator's, it will rank higher because it will likely have more favorable validators. Definitely, this approach can be improved and strengthened. We could sample distances to a target pubkey from different direct contacts of the source pubkey to get more paths validated. The architecture will allow this thanks to the Nostr social graph lib by @Martti Malmi that we are using. Thanks for the feedback; it is very appreciated. As we mentioned, this is still an experiment, and we need to keep tweaking it to get more relevant results and metrics.

They are based on the social graph distance between you and the source pubkey, as well as other objective validation metrics like nip05, some events, LN address, etc. You can find more detailed information in the article or the about page. https://relatr.xyz/about

Okay - it seems my score was based on social media - I got banned from.. Hence the entire reason I thought I was safe here..

This is a totally subjective metric; nothing related to social media platforms works. The rank number is not relevant; the relevance is that your pubkey will rank higher than someone trying to impersonate you.

Okay thank you very much. ContextVM. I see the benefit! We all need to look around and see what we are creating with AI / Blockchain / crypto tech.

Great idea! We will open an issue to track the progress of this feature

Yes, Relatr is a CVM server available for everyone. You can integrate it into any client. Here is an example of how to integrate it easily into any TypeScript/JavaScript project. In reality, it's just JSON-RPC over Nostr, so you could integrate it in any language really. Little by little, we will be bringing more tools to make it as easy in other lenguage as it is in ts projects.