A Graph Autoencoder Approach to Crowdsourcing

Crowdsourcing deals with combining and aggregating labels from crowds of annotators of unknown reliability. While most works on label aggregation operate under the assumption of independent and identically distributed data, the present work introduces an algorithm that operates under known data dependencies or correlations. To exploit these dependencies, a novel graph autoencoder-based algorithm is developed that fuses annotator labels for crowdsourced classification tasks. Numerical tests on real data showcase the potential of the proposed approach.