EigenTrust

Brief explanation of EigenTrust and How it works.

EigenTrust works best with datasets that involve trust and reputation systems, such as peer-to-peer networks. These datasets typically include user interactions, ratings, and feedback, which allow the algorithm to compute trust scores effectively.

EigenTrust helps networked peers measure the level of trust placed on one another in a peer-to-peer network. The core idea behind EigenTrust is that a person’s reputation is defined recursively by the people who trust that person, weighted by those people’s reputations.

As a baseline, you can trust your friends. This gives you a good starting point, but because each person only has so many friends, it’s too limited to make a reliable system for millions of users/peers in a network. As a next step, you can expand that by asking your friends who they trust, and weighing their opinions by how much you trust your friends.

The linear algebra behind EigenTrust — you can initialize a trust vector with a set of seed peers that you trust. And then you can keep multiplying that vector by a matrix that represents the pairwise trust judgments of all the peers in the network. This is a power method algorithm, and it converges to the principal eigenvector of the matrix. Eventually you get complete coverage over everyone connected to you, directly or indirectly - in just a single eigenvector calculation.

Local Trust (Reputation Graphs)

Local Trust values (LTVs) measure direct trust between peers, which can be asymmetric, and are crucial for context-specific activities like social networks, on-chain token transfers, and reputational attestations. This is the dataset that is usually set as the Input with the format of i, j, v === from, to, value

Seed Trust

Seed trust allows services to prioritize trust opinions from specific peers or networks, enhancing resistance to Sybil attacks. By incorporating seed trust and confidence as parameters, tailored trust scores can be achieved, ensuring better network integrity.