Trust Propagation
How trust spreads across the Local Protocol network based on transaction history and graph connectivity, diminishing over longer paths from trusted sources.
Trust propagation in the Local Protocol allows trust to spread across the network based on transaction history and graph connectivity. The concept ensures that trust diminishes gradually as it travels further from a trusted source node, enabling the protocol to assess participant reliability over time.
This mechanism helps the network establish broader trust networks, making it harder for malicious actors to gain undue influence without genuine connectivity.
See: The Transaction Graph and Snapshot-Relative Diffusion.
Key Concepts
- Decaying Trust: Trust assumptions weaken over longer paths from the source.
- Network-Wide Impact: Trust spreads through the transaction graph, enhancing overall reliability.
Trust under snapshot-relative diffusion
Under diffusion, proofs inject trust in protocol-defined ways:
- Seed mass (teleport) updates: proofs can increase
(or inclusion in the seed set) for verified identities/domains, in market context . - Edge weight adjustments: service proofs and dispute outcomes modify edge weights via
proof_factorandquality.
See: Service Proofs and Dispute Resolution & Collateral.
The effect of a proof naturally diminishes with path length: in a restarted random walk, influence along length-
You can visualize the network as a series of concentric circles centered around the node or edge that incorporated a proof. The nodes directly connected form the first circle; these are the immediate neighbors who have direct interactions with the proof-bearing node or transaction. The second circle consists of nodes connected to those immediate neighbors, which are two steps away, and so on. The influence of the proof is strongest at the center and decreases as you move outward.
Nodes that transact with those who submit strong proofs benefit more than they would have otherwise. This produces positive security externalities: when self-interested actors invest in verifiability, the network becomes more trustworthy and rewards become more robust.
Next Steps
In the next section, we will examine Sampling & Slashing, the mechanism used to verify bounded claims and penalize dishonest behavior.