Name disambiguation method based on hyperbolic space feature fusion
In view of the challenge of name duplication and the increasingly serious influence of name ambiguity in tra-ditional user influence analysis and other research,the impact of name ambiguity is becoming increasingly serious.This paper proposes a network alignment model-geometry interaction network alignment(GINA)based on the fusion of hy-perbolic space and Euclidean space features,fusing multiple spatial features.It effectively establishes a model to show the main function of a network structure for name disambiguation.The fundamental idea of this paper is to simultan-eously utilize both Euclidean space and hyperbolic space for network representation learning,aiming to capture network structural information with distinct spatial characteristics.It employs cross-space network mapping and cross-space fea-ture fusion to realize information exchange among different spaces and final network representation under the situations of reducing loss of spatial mapping as far as possible,implements network alignment and further name disambiguation.By performing network alignment based on the obtained representations,the paper accomplishes name disambiguation.On real datasets,the Chinese paper co-authorship network,English paper co-authorship network,and the Chinese patent co-authorship network are aligned in pair to construct the"Paper-Patent"empirical data network alignment dataset and the"Chinese-English"empirical data network alignment dataset to carry out the test demonstration of GINA model in two empirical scenarios for the identity recognition of the individuals with the same name and Chinese&foreign papers.The results show that the precision in the hyperbolic space combined with the Euclidean space is at least 24.9%higher than that in a single space,confirming effectiveness of the GINA method.
name disambiguationEuclidean spacehyperbolic spacenetwork alignmentnetwork representation learn-inggraph embeddingfeature fusionanchor link prediction
万方数据
维普
