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Special Issue: Artificial Intelligence and Pattern Recognition

• Artificial Intelligence and Pattern Recognition •

### BHONEM: Binary High-Order Network Embedding Methods for Networked-Guarantee Loans

Da-Wei Cheng1, Member, CCF, Yi Tu1, Zhen-Wei Ma2, Zhi-Bin Niu3, Member, CCF, Li-Qing Zhang1,*, Member, ACM, IEEE

1. 1 Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
2 School of Mathematical Science, Shanghai Jiao Tong University, Shanghai 200240, China;
3 School of Computer Software, Tianjin University, Tianjin 300354, China
• Received:2018-05-28 Revised:2019-03-17 Online:2019-05-05 Published:2019-05-06
• Contact: Li-Qing Zhang E-mail:zhang-lq@cs.sjtu.edu.cn
• About author:Da-Wei Cheng is a Ph.D. candidate in Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai. He received his Bachelor's degree in industrial design from Nanjing University of Aernautics and Astronautics, Nanjing, in 2008. His research fields include machine learning, financial data mining, knowledge discovery and time series pattern recognition.
• Supported by:
The work was supported by the National Basic Research 973 Program of China under Grant No. 2015CB856004 and the Key Basic Research Program of Shanghai Science and Technology Commission of China under Grant Nos. 15JC1400103 and 16JC1402800.

Networked-guarantee loans may cause systemic risk related concern for the government and banks in China. The prediction of the default of enterprise loans is a typical machine learning based classification problem, and the networked guarantee makes this problem very difficult to solve. As we know, a complex network is usually stored and represented by an adjacency matrix. It is a high-dimensional and sparse matrix, whereas machine-learning methods usually need lowdimensional dense feature representations. Therefore, in this paper, we propose a binary higher-order network embedding method to learn the low-dimensional representations of a guarantee network. We first set vertices of this heterogeneous economic network by binary roles (guarantor and guarantee), and then define high-order adjacent measures based on their roles and economic domain knowledge. Afterwards, we design a penalty parameter in the objective function to balance the importance of network structure and adjacency. We optimize it by negative sampling based gradient descent algorithms, which solve the limitation of stochastic gradient descent on weighted edges without compromising efficiency. Finally, we test our proposed method on three real-world network datasets. The result shows that this method outperforms other start-of-the-art algorithms for both classification accuracy and robustness, especially in a guarantee network.

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