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CLC number: TP391

On-line Access: 2024-08-27

Received: 2023-10-17

Revision Accepted: 2024-05-08

Crosschecked: 2022-12-01

Cited: 0

Clicked: 2047

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zhiqiang ZHANG

https://orcid.org/0000-0001-5483-0366

Qianqiao LIANG

https://orcid.org/0000-0002-6205-988X

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Frontiers of Information Technology & Electronic Engineering  2023 Vol.24 No.3 P.388-402

http://doi.org/10.1631/FITEE.2200151


Exploring financially constrained small- and medium-sized enterprises based on a multi-relation translational graph attention network


Author(s):  Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN

Affiliation(s):  College of Computer Science and Technology, Zhejiang University, Hangzhou 310000, China; more

Corresponding email(s):   xlzheng@zju.edu.cn

Key Words:  Financing needs exploration, Graph representation learning, Transfer heterogeneity, Behavior heterogeneity


Qianqiao LIANG, Hua WEI, Yaxi WU, Feng WEI, Deng ZHAO, Jianshan HE, Xiaolin ZHENG, Guofang MA, Bing HAN. Exploring financially constrained small- and medium-sized enterprises based on a multi-relation translational graph attention network[J]. Frontiers of Information Technology & Electronic Engineering, 2023, 24(3): 388-402.

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journal="Frontiers of Information Technology & Electronic Engineering",
volume="24",
number="3",
pages="388-402",
year="2023",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.2200151"
}

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Abstract: 
financing needs exploration (FNE), which explores financially constrained small- and medium-sized enterprises (SMEs), has become increasingly important in industry for financial institutions to facilitate SMEs' development. In this paper, we first perform an insightful exploratory analysis to exploit the transfer phenomenon of financing 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 aTtention network (M-RIGHT), which not only models the transfer heterogeneity of financing 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.

基于多关系平移图注意力网络的需融中小企业挖掘方法

梁倩乔1,卫华2,吴亚熙2,韦峰2,赵登3,何建衫3,郑小林1,马国芳4,韩冰2
1浙江大学计算机科学与技术学院,中国杭州市,310000
2蚂蚁集团网商银行,中国杭州市,310000
3蚂蚁集团蚂蚁智能引擎技术事业部,中国杭州市,310000
4浙江工商大学计算机与信息工程学院,中国杭州市,310000
摘要:需融企业挖掘任务指挖掘财务困难、需要融资的中小企业,其有利于金融机构帮助中小企业的发展,在金融领域中发挥着越来越重要的作用。本文首先深入分析中小企业的融资需求在企业社交网络中的传递现象,从而提供了一个利用企业社交网络来提升需融企业挖掘有效性的想法,即学习企业社交网络中企业节点的表征并利用该表征挖掘需融企业。然而,该想法面临两种异构性挑战,即融资需求在不同关系下的传递异构性和中小企业在不同关系类型下的行为模式异构性。为了应对这些挑战,本文提出一种基于多关系平移图注意力网络的需融企业挖掘方法。该方法不仅基于一种新颖的实体-关系组合算子来对不同关系类型下的融资需求传递异构性进行建模,还基于本文所设计的关系超平面平移机制来获得中小企业在不同关系类型下的表征,以区分中小企业在不同关系类型下的异构行为模式。最后,本文在两个大型真实数据集上进行大量实验,实验结果证明了所提方法在需融企业挖掘任务中优于最先进的方法。

关键词:需融企业挖掘;图表征学习;传递异构性;行为模型异构性

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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