Learning Joint Relational Co-evolution in Spatial-Temporal Knowledge Graph for SMEs Supply Chain Prediction-Reference-Cited by-同舟云学术

Learning Joint Relational Co-evolution in Spatial-Temporal Knowledge Graph for SMEs Supply Chain Prediction

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Li Youru¹^ORCID,Zhu Zhenfeng¹^ORCID,Guo Xiaobo²^ORCID,Chen Linxun³^ORCID,Wang Zhouyin⁴^ORCID,Wang Yinmeng⁵^ORCID,Han Bing³^ORCID,Zhao Yao¹^ORCID

Affiliation:

1. Beijing Jiaotong University, Beijing, China

2. Beijing Jiaotong University & MYBank, Ant Group, Beijing, China

3. MYBank, Ant Group, Beijing, China

4. MYBank, Ant Group, Hangzhou, China

5. MYBank, Ant Group, Shanghai, China

Funder

Science and Technology Innovation 2030 ? New Generation Artificial Intelligence Major Project of China

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599855

Reference45 articles.

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3. Logistic regression and artificial neural network classification models: a methodology review

4. Pankaj Dutta , Tsan-Ming Choi , Surabhi Somani , and Richa Butala . 2020. Blockchain technology in supply chain operations: Applications, challenges and research opportunities. Transportation research part e: Logistics and transportation review 142 ( 2020 ), 102067. Pankaj Dutta, Tsan-Ming Choi, Surabhi Somani, and Richa Butala. 2020. Blockchain technology in supply chain operations: Applications, challenges and research opportunities. Transportation research part e: Logistics and transportation review 142 (2020), 102067.

5. Jerome H Friedman . 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics ( 2001 ), 1189--1232. Jerome H Friedman. 2001. Greedy function approximation: a gradient boosting machine. Annals of statistics (2001), 1189--1232.

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