Using Graph Data Science to compute a reputation score (betweenness centrality) on Mirror user interactions across Twitter, Governance, and Ethereum transactions
This post was first published on ath.mirror.xyz, be sure to subscribe there and follow me on twitter to get my most up-to-date crypto and data science content. Later on, this methodology was used for the first $WRITE token airdrop which you can read about here.
In my previous post on digital identity, I mentioned that “the tokenization of these graph shards could take many forms and will likely be layered upon by proof tokens.” I believe that sharded graph identity approach requires coming up with a community-specific reputation score that measures how influential a certain person has been in expanding a specific network. While some reputation scores may be more set in terms of having to have done X or Y actions, this score captures a users reputation in the context of other users in a more fluid manner — acting like a signal rather than a badge.
Typically in Web2, users are “rewarded” by an algorithm which will highlight them based on the engagement and attention they bring to the platform. Thus, their reputation score is just the number of likes or followers they have — regardless of who those vanity metrics come from. In Web3, we typically reward with tokens representing the value of the protocol or product. These tokens also carry a lot of power in the form of voting and other privileges. As such, a score that signals not only influence but also how supportive or aligned a user is with the rest of the community will become increasingly important over time.
For this post, we will be focusing on creating a reputation score for Mirror users (voters, writers, contributors) based on where each user sits on an interaction-based social graph across Ethereum, Twitter, and Mirror data. I chose Mirror for three reasons:
- I’m already very familiar with their community and product
- They have verifiable consolidation of identity on their platform (Ethereum <> Twitter), allowing me to layer social graphs together.
- Their product and governance are heavily user-to-user interaction focused, something most products on Ethereum don’t currently have (we mostly interact with pools or marketplace protocols).
Social graphs themselves are not new, but selectively layering them will open up quite a few new doors of usability and meaningfulness. There are two main reasons for this:
- Enabling new applications:…
Continue reading: https://towardsdatascience.com/creating-a-community-specific-reputation-score-for-users-of-web3-platform-mirror-xyz-14d494a25ea8?source=rss—-7f60cf5620c9—4