RR-GNN uses a residual-predicting GNN and graph clustering to produce tighter conformal prediction intervals for GNN outputs while preserving marginal coverage guarantees.
Mitigating Misinformation Spread on Blockchain Enabled Social Media Networks
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abstract
The paper develops a blockchain protocol for a social media network (BE-SMN) to mitigate the spread of misinformation. BE-SMN is derived based on the information transmission-time distribution by modeling the misinformation transmission as double-spend attacks on blockchain. The misinformation distribution is then incorporated into the SIR (Susceptible, Infectious, or Recovered) model, which substitutes the single rate parameter in the traditional SIR model. Then, on a multi-community network, we study the propagation of misinformation numerically and show that the proposed blockchain enabled social media network outperforms the baseline network in flattening the curve of the infected population.
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cs.LG 1years
2025 1verdicts
REJECT 1representative citing papers
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Residual Reweighted Conformal Prediction for Graph Neural Networks
RR-GNN uses a residual-predicting GNN and graph clustering to produce tighter conformal prediction intervals for GNN outputs while preserving marginal coverage guarantees.