Abstract: In the era of big data,the release of data value often requires the fusion of multi-source data,and data conflict has become an inevitable key problem in this process.In order to filter out true claims and reliable sources from conflicting data,researchers 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 information,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 cooperative information and antagonistic information for of each node,and a truth discovery variational auto-encoder is designed to extract 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 effectiveness and generalization ability of the method are verified by ablation experiments and visualization.

Key words: Data quality, Conflict resolution, Truth discovery, Multi-hop attention graph neural network, Variational auto-encoder