Social network graphs help us understand and analyze interactions within large communities. These graphs, characterized by nodes representing individuals and edges denoting relationships or interactions, reveal intricate patterns of influence and connection. Within such networks, identifying key players—hubs and authorities—is crucial for various applications, from marketing strategies to information dissemination to trust evaluation.
Likewise, in the context of BUILD, understanding user interactions and nominations, mirrors the properties of social network graphs. Here, hubs—users who initiate numerous connections—and authorities—users who receive many endorsements—helps us to understand which builder is deemed influential and hence likely deemed trustworthy by others.
However, it is undesirable to reward reciprocation (i.e. I reward you, you reward me) as this turns into a collaboration game rather than expression of the trustworthiness of a builder. Therefore, we believe it makes sense to penalize overly reciprocal behavior. Our algorithm aims to distinguish genuine influence from mere social exchanges, thereby providing a clearer picture of each user's true standing within the network.
We built a directed graph where nodes represent users and edges represent interactions, with weights corresponding to the strength of these interactions. We then apply the HITS algorithm to compute two numbers for a node. Authorities estimate the node value based on the incoming links. Hubs estimates the node value based on outgoing links. Consequently we analyze the mutual interactions (reciprocations) between users. The reciprocity count for a user is the number of users who have reciprocal interactions with them.
Finally we arrive at a trust score for a given node:
Where:
α is a parameter that balances the hub and authority scores.
hub(u) and authority(u) are the hub and authority scores of user u obtained from the HITS algorithm.
reciprocitycount(u) is the count of mutual reciprocations for user u.
In order to evaluate the algorithms we relied on labels to see how effective the developed algorithm compared to these. For labels we observed which quantiles users were placed based on their trust score.
Labels:
farmer - we curated a list of known farmers which aided the evaluation of the score
builders - we manually curated a list of builder deemed trustworthy
farcaster_power_user - we extracted a list of farcaster power users
profile_completeness - while far from perfect we created a score on profile completeness (i.e. if they hold a talent passport, farcaster profile and have a dedicated username)
While we tested several algorithms (semantic analysis, eigentrust, peertrust, mDC) we found that this implementation performed best. Below you can observe the results.
From the chart we see that it generally places users showing signs of high trustworthiness in higher quantiles while not overly encouraging farmers.
BUILD operates already with an existing Builder Score, if we wanted to integrate the HITS algorithm with reciprocation penalty we simply would assign a weight to the existing Builder Score and to the developed algorithm.
Let’s assume we wanted to start conservatively and assign a weight of 0.1 to the developed algorithm. We simply would normalize the existing builder score between 0 and 1 and then normalize the HITS-RP.
Given the overall score is already normalized between 0 to 1 we can simply multiply it by the overall budget to calculate the budget distribution for a weekly Build airdrop.
Big thank you to Thomas Eisermann for his work on this research.
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The build.top leaderboard is a joke. I’ve clicked on every name on there and most of them are farm accounts, not builders. I sold all of my remaining tokens except for the minimum to keep nominating people. I stealth nominate one person per week in good faith but I’m not sure this deserves my attention anymore. I’d love to hear other opinions.
They mention here that they use an algo to squash farmers... https://paragraph.xyz/@macedo/build-ranking-algo But the leaderboard does not reflect this at all, full of farmers. The season 1 leaderboard looked like this, the season 2 leaderboard also looked like this from week 1. At that point I stopped playing the game. I fee like I've just committed by 70m tokens to farmers.
Same. Kicking myself for falling for this one.
reeeeeeeeee 😭😭😭😭😭😭
Well maybe only actual builder should be able to be nominated !! Shouldn’t be too difficult to actually put restrictions on it
Now the nomination seems to have deteriorated, people are more interested in tokens rather than discovering good builders..
Also, the claim says you have to have a Talent Passport, and one can have a Gitcoin Identity of 15 or higher, mine is 33 yet I'm still disqualified.
Seeing as the founder is one of the most blocked people on the protocol, I’d say sentiment is poor.
I didn't know that, and now I do. Also, the first complaint I saw was yours - leaderboard full of bots - but supposedly they have this anti-bot algo, which includes "human checkmark". How bots get the human checkmark while actual humans (like me) who have exceeded their human checkmark requirements, are denied the airdrop sorry, I mean unfAIRDROP
might just be my feed, but haven't seen much from the team post-Airdrop 1 lockdrop. was hoping they'd come out strong with season 2 and explore how the token could really target builders who need support. sad to see its getting farmed so aggressively
I assumed it would be farmed but not like this. This is ridiculous.
Ohhh i have not payes attention to this anymore, i think is the same for everyone else too thats why farmers are doing great?
Really fell off after we all donated our airdrop 😂 😭
I gave up on this, coz u never know, they might ask u to donate ur build for builders, & all our time gets wasted...
I’m sure they will ask us to donate…again….and the cycle will begin again.
Felt like work. I get enough of that feeling at work.