DeTox-Fed uses federated graph neural networks on local conversation graphs to detect toxic discussions in the Fediverse while keeping all raw data and labels on individual instances.
Journal of Complex Networks , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Bayesian latent space models for graphs are misspecified on real data, leading to poor calibration; a new generalized posterior with adaptive regularization via prequential risk estimation improves performance and geometry choice.
citing papers explorer
-
DeTox-Fed: Detecting Toxic Conversations in the Fediverse with Federated Graph Neural Networks
DeTox-Fed uses federated graph neural networks on local conversation graphs to detect toxic discussions in the Fediverse while keeping all raw data and labels on individual instances.
-
Bayesian Latent Space Models for Graphs Are Misspecified: Toward Robust Inference via Generalized Posteriors
Bayesian latent space models for graphs are misspecified on real data, leading to poor calibration; a new generalized posterior with adaptive regularization via prequential risk estimation improves performance and geometry choice.