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CLC number: TP391

On-line Access: 2023-03-25

Received: 2022-04-14

Revision Accepted: 2023-03-25

Crosschecked: 2022-12-01

Cited: 0

Clicked: 1226

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",
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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

Reference

[1]Angilella S, Mazzù S, 2015. The financing of innovative SMEs: a multicriteria credit rating model. Eur J Oper Res, 244(2):540-554.

[2]Bordes A, Usunier N, García-Durán A, et al., 2013. Translating embeddings for modeling multi-relational data. Proc 26th Int Conf on Neural Information Processing Systems, p.2787-2795.

[3]Ceptureanu EG, Ceptureanu S, Herteliu C, 2021. Evidence regarding external financing in manufacturing MSEs using partial least squares regression. Ann Oper Res, 299(1-2):1189-1202.

[4]Chen XF, Zhao YD, Wei ZF, et al., 2020. Intelligent identification of potential customers for electricity substitution. In: Tallón-Ballesteros AJ (Ed.), Fuzzy Systems and Data Mining, VI. IOS Press, p.168-179.

[5]Cheng HT, Koc L, Harmsen J, et al., 2016. Wide & deep learning for recommender systems. Proc 1st Workshop on Deep Learning for Recommender Systems, p.7-10.

[6]Dettmers T, Minervini P, Stenetorp P, et al., 2018. Convolutional 2D knowledge graph embeddings. Proc 32nd AAAI Conf on Artificial Intelligence, p.1811-1818.

[7]Dong YX, Chawla NV, Swami A, 2017. metapath2vec: scalable representation learning for heterogeneous networks. Proc 23rd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.135-144.

[8]Duan GL, Ma X, 2018. A coupon usage prediction algorithm based on XGBoost. Proc 14th Int Conf on Natural Computation, Fuzzy Systems and Knowledge Discovery, p.178-183.

[9]Fu XY, Zhang JN, Meng ZQ, et al., 2020. MAGNN: meta- path aggregated graph neural network for heterogeneous graph embedding. Proc Web Conf, p.2331-2341.

[10]Graesch JP, Hensel-Börner S, Henseler J, 2021. Information technology and marketing: an important partnership for decades. Ind Manag Data Syst, 121(1):123-157.

[11]Grover A, Leskovec J, 2016. node2vec: scalable feature learning for networks. Proc 22nd ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.855-864.

[12]Guo HF, Tang RM, Ye YM, et al., 2017. DeepFM: a factorization-machine based neural network for CTR prediction. Proc 26th Int Joint Conf on Artificial Intelligence, p.1725-1731.

[13]Hamilton WL, Ying Z, Leskovec J, 2017. Inductive representation learning on large graphs. Proc 31st Int Conf on Neural Information Processing Systems, p.1025-1035.

[14]Jeon H, 2021. Investment and financing decisions in the presence of time-to-build. Eur J Oper Res, 288(3):1068-1084.

[15]Ji SX, Pan SR, Cambria E, et al., 2022. A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans Neur Netw Learn Syst, 33(2):494-514.

[16]Kipf TN, Welling M, 2017. Semi-supervised classification with graph convolutional networks. Proc 5th Int Conf on Learning Representations, p.1-10.

[17]Kshetri N, 2016. Big data's role in expanding access to financial services in China. Int J Inform Manag, 36(3):297-308.

[18]Lessmann S, Haupt J, Coussement K, et al., 2021. Targeting customers for profit: an ensemble learning framework to support marketing decision-making. Inform Sci, 557:286-301.

[19]Li ZF, Liu H, Zhang ZL, et al., 2022. Learning knowledge graph embedding with heterogeneous relation attention networks. IEEE Trans Neur Netw Learn Syst, 33(8):3961-3973.

[20]Liao HF, Hu J, Li TR, et al., 2022. Deep linear graph attention model for attributed graph clustering. Knowl-Based Syst, 246:108665.

[21]Luo Y, Deng TY, Wei Q, et al., 2021. Optimal financing decision in a contract food supply chain with capital constraint. Complexity, 2021:8925102.

[22]Maas AL, Hannun AY, Ng AY, 2013. Rectifier nonlinearities improve neural network acoustic models. Proc 30th Int Conf on Machine Learning, p.1-6.

[23]Mikolov T, Chen K, Corrado G, et al., 2013. Efficient estimation of word representations in vector space. Proc 1st Int Conf on Learning Representations, p.1-10.

[24]Nickel M, Rosasco L, Poggio T, 2016. Holographic embeddings of knowledge graphs. Proc 30th AAAI Conf on Artificial Intelligence, p.1955-1961.

[25]Perozzi B, Al-Rfou R, Skiena S, 2014. DeepWalk: online learning of social representations. Proc 20th ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.701-710.

[26]Rogic S, Kascelan L, 2019. Customer value prediction in direct marketing using hybrid support vector machine rule extraction method. Proc 23rd European Conf on Advances in Databases and Information Systems, p.283-294.

[27]Rogić S, Kašćelan L, Pejić Bach M, 2022. Customer response model in direct marketing: solving the problem of unbalanced dataset with a balanced support vector machine. J Theor Appl Electron Commer Res, 17(3):1003-1018.

[28]Sadeghian A, Armandpour M, Colas A, et al., 2021. ChronoR: rotation based temporal knowledge graph embedding. Proc AAAI Conf on Artificial Intelligence, p.6471-6479.

[29]Schlichtkrull M, Kipf TN, Bloem P, et al., 2018. Modeling relational data with graph convolutional networks. Proc 15th Int Conf on Semantic Web Conf, p.593-607.

[30]Shang C, Tang Y, Huang J, et al., 2019. End-to-end structure-aware convolutional networks for knowledge base completion. Proc AAAI Conf on Artificial Intelligence, p.3060-3067.

[31]Shi Y, Gui H, Zhu Q, et al., 2018a. ASPEM: embedding learning by aspects in heterogeneous information networks. Proc SIAM Int Conf on Data Mining, p.144-152.

[32]Shi Y, Zhu Q, Guo F, et al., 2018b. Easing embedding learning by comprehensive transcription of heterogeneous information networks. Proc 24th ACM SIGKDD Int Conf on Knowledge Discovery & Data Mining, p.2190-2199.

[33]Sun ZQ, Deng ZH, Nie JY, et al., 2019. Rotate: knowledge graph embedding by relational rotation in complex space. Proc 7th Int Conf on Learning Representations, p.1-10.

[34]Tang J, Qu M, Mei QZ, 2015. PTE: predictive text embedding through large-scale heterogeneous text networks. Proc 21st ACM SIGKDD Int Conf on Knowledge Discovery and Data Mining, p.1165-1174.

[35]Tian Z, Hassan AFS, Razak NHA, 2018. Big data and SME financing in China. J Phys Conf Ser, 1018:012002.

[36]Trouillon T, Welbl J, Riedel S, et al., 2016. Complex embeddings for simple link prediction. Proc 33rd Int Conf on Machine Learning, p.2071-2080.

[37]Vashishth S, Sanyal S, Nitin V, et al., 2020a. Composition-based multi-relational graph convolutional networks. Proc 8th Int Conf on Learning Representations, p.1-10.

[38]Vashishth S, Sanyal S, Nitin V, et al., 2020b. InteractE: improving convolution-based knowledge graph embeddings by increasing feature interactions. Proc AAAI Conf on Artificial Intelligence, p.3009-3016.

[39]Veličković P, Cucurull G, Casanova A, et al., 2018. Graph attention networks. Proc 6th Int Conf on Learning Representations, p.1-10.

[40]Wang X, Zhang YD, Shi C, 2019. Hyperbolic heterogeneous information network embedding. Proc AAAI Conf on Artificial Intelligence, p.5337-5344.

[41]Wang Y, Jing CF, Xu SS, et al., 2022. Attention based spatiotemporal graph attention networks for traffic flow forecasting. Inform Sci, 607:869-883.

[42]Wang Z, Zhang JW, Feng JL, et al., 2014. Knowledge graph embedding by translating on hyperplanes. Proc 28th AAAI Conf on Artificial Intelligence, p.1112-1119.

[43]Wu ZH, Pan SR, Chen FW, et al., 2021. A comprehensive survey on graph neural networks. IEEE Trans Neur Netw Learn Syst, 32(1):4-24.

[44]Xu KYL, Hu WH, Leskovec J, et al., 2019. How powerful are graph neural networks? Proc 7th Int Conf on Learning Representations, p.1-10.

[45]Xu ZP, Meisami A, Tewari A, 2021. Decision making problems with funnel structure: a multi-task learning approach with application to email marketing campaigns. Proc 24th Int Conf on Artificial Intelligence and Statistics, p.127-135.

[46]Yang BS, Yih SWT, He XD, et al., 2015. Embedding entities and relations for learning and inference in knowledge bases. Proc 3rd Int Conf on Learning Representations, p.1-10.

[47]Yang C, Xiao YX, Zhang Y, et al., 2022. Heterogeneous network representation learning: a unified framework with survey and benchmark. IEEE Trans Knowl Data Eng, 34(10):4854-4873.

[48]Yang S, Zhang ZQ, Zhou J, et al., 2020. Financial risk analysis for SMEs with graph-based supply chain mining. Proc 29th Int Joint Conf on Artificial Intelligence, p.4661-4667.

[49]Ye R, Li X, Fang YJ, et al., 2019. A vectorized relational graph convolutional network for multi-relational network alignment. Proc 28th Int Joint Conf on Artificial Intelligence, p.4135-4141.

[50]Yu PY, Fu CF, Yu YW, et al., 2022. Multiplex heterogeneous graph convolutional network. Proc 28th ACM SIGKDD Conf on Knowledge Discovery and Data Mining, p.2377-2387.

[51]Zhang B, Wang LQ, Li YY, 2021. Precision marketing method of e-commerce platform based on clustering algorithm. Complexity, 2021:5538677.

[52]Zhang WT, Fang Y, Liu ZM, et al., 2022. mg2vec: learning relationship-preserving heterogeneous graph representations via metagraph embedding. IEEE Trans Knowl Data Eng, 34(3):1317-1329.

[53]Zhao J, Wang X, Shi C, et al., 2021. Heterogeneous graph structure learning for graph neural networks. Proc AAAI Conf on Artificial Intelligence, p.4697-4705.

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