Deep Collaborative Truth Discovery Based on Variational Multi-hop Graph Attention Encoder
In the era of big data,the release of data value often requires the fusion of multi-source data,and data conflict has be-come an inevitable key problem in this process.In order to filter out true claims and reliable sources from conflicting data,re-searchers have proposed truth discovery methods.However,the existing truth discovery methods pay more attention to the direct collaborative information between sources and claims,and ignore the deeper indirect collaborative and confrontational informa-tion,which is insufficient to express the characteristics of sources and claims.To solve this problem,this paper proposes a truth discovery method based on variational multi-hop graph attention encoder(TD-VMGAE).It constructs a bipartite graph network based on the inclusion relationship between sources and claims,uses a multi-hop graph attention layer to gather indirect coopera-tive information and antagonistic information for of each node,and a truth discovery variational auto-encoder is designed to ex-tract the categorical distribution required in node characterization,and collaborative classification of data sources and claims is carried out.Experiments show that the proposed method has good performance in three datasets with different scales,and the ef-fectiveness and generalization ability of the method are verified by ablation experiments and visualization.
Data qualityConflict resolutionTruth discoveryMulti-hop attention graph neural networkVariational auto-encoder
万方数据
维普
