Financing needs exploration(FNE),which exploresfinancially constrained small-and medium-sized enterprises(SMEs),has become increasingly important in industry forfinancial institutions to facilitate SMEs’development.I...Financing needs exploration(FNE),which exploresfinancially constrained small-and medium-sized enterprises(SMEs),has become increasingly important in industry forfinancial institutions to facilitate SMEs’development.In this paper,wefirst perform an insightful exploratory analysis to exploit the transfer phenomenon offinancing needs among SMEs,which motivates us to fully exploit the multi-relation enterprise social network for boosting the effectiveness of FNE.The main challenge lies in modeling two kinds of heterogeneity,i.e.,transfer heterogeneity and SMEs’behavior heterogeneity,under different relation types simultaneously.To address these challenges,we propose a graph neural network named Multi-relation tRanslatIonal GrapH a Ttention network(M-RIGHT),which not only models the transfer heterogeneity offinancing needs along different relation types based on a novel entity–relation composition operator but also enables heterogeneous SMEs’representations based on a translation mechanism on relational hyperplanes to distinguish SMEs’heterogeneous behaviors under different relation types.Extensive experiments on two large-scale real-world datasets demonstrate M-RIGHT’s superiority over the state-of-the-art methods in the FNE task.展开更多
基金Project supported in part by the National Natural Sci-ence Foundation of China(No.72192823)the“Ten Thousand Talents Program”of Zhejiang Province for Leading Experts(No.2021R52001)the Cooperation Project of MYbank,Ant Group。
文摘Financing needs exploration(FNE),which exploresfinancially constrained small-and medium-sized enterprises(SMEs),has become increasingly important in industry forfinancial institutions to facilitate SMEs’development.In this paper,wefirst perform an insightful exploratory analysis to exploit the transfer phenomenon offinancing needs among SMEs,which motivates us to fully exploit the multi-relation enterprise social network for boosting the effectiveness of FNE.The main challenge lies in modeling two kinds of heterogeneity,i.e.,transfer heterogeneity and SMEs’behavior heterogeneity,under different relation types simultaneously.To address these challenges,we propose a graph neural network named Multi-relation tRanslatIonal GrapH a Ttention network(M-RIGHT),which not only models the transfer heterogeneity offinancing needs along different relation types based on a novel entity–relation composition operator but also enables heterogeneous SMEs’representations based on a translation mechanism on relational hyperplanes to distinguish SMEs’heterogeneous behaviors under different relation types.Extensive experiments on two large-scale real-world datasets demonstrate M-RIGHT’s superiority over the state-of-the-art methods in the FNE task.