Enforcing Trust Accountability with Backward Propagation

Trust and reputation management underpins reliable interactions in distributed networks, yet existing trust models rely solely on forward propagation of interaction-based trust signals. They lack robust mechanisms to enforce accountability for the propagated trust signals when negative interactions occur. In addition, such models often fail to initialize newly joined nodes with sparse interaction history, leading to the cold-start problem. In this paper, we propose RepuLink, a two-layer reputation model that couples an endorsement network with an interaction feedback network. RepuLink integrates two concurrent backward propagation mechanisms: Backward Endorsement Penalty Propagation (BEPP), which recursively penalizes endorsers of misbehaving nodes, and Backward Endorsement Reward Propagation (BERP), which rewards endorsers of well-performing nodes. Together, RepuLink enforces endorsement accountability and incentivizes positive behaviors, which form a positive interaction feedback loop. The endorsement layer further provides explainable, endorser-weighted trust initialization for newly joined nodes. Experiments on real-world datasets against representative trust propagation baselines demonstrate that RepuLink outperforms across four evaluation metrics in both interaction-only and full two-layer settings, while preserving comparable efficiency.

• Computing methodologies → Model development and analysis; • Networks → Peer-to-peer networks.