• Research Paper •

### Parameterless Pruning Algorithms for Similarity-Weight Network and Its Application in Extracting the Backbone of Global Value Chain

Lizhi Xing1,2,†, Yu Han1

1. 1College of Economics and Management, Beijing University of Technology, Beijing 100124, China;
2International Business School, Beijing Foreign Studies University, Beijing 100089, China
• Received:2021-07-11 Revised:2021-10-02 Accepted:2021-10-11
• Contact: Lizhi Xing (Email: itwasa@163.com, ORCID: 0000-0001-9554-5414).

Abstract: Purpose: With the availability and utilization of Inter-Country Input-Output (ICIO) tables, it is possible to construct quantitative indices to assess its impact on the Global Value Chain (GVC). For the sake of visualization, ICIO networks with tremendous low- weight edges are too dense to show the substantial structure. These redundant edges, inevitably make the network data full of noise and eventually exert negative effects on Social Network Analysis (SNA). In this case, we need a method to filter such edges and obtain a sparser network with only the meaningful connections.
Design/methodology/approach: In this paper, we propose two parameterless pruning algorithms from the global and local perspectives respectively, then the performance of them is examined using the ICIO table from different databases.
Findings: The Searching Paths (SP) method extracts the strongest association paths from the global perspective, while Filtering Edges (FE) method captures the key links according to the local weight ratio. The results show that the FE method can basically include the SP method and become the best solution for the ICIO networks.
Research limitations: There are still two limitations in this research. One is that the computational complexity may increase rapidly while processing the large-scale networks, so the proposed method should be further improved. The other is that much more empirical networks should be introduced to testify the scientificity and practicability of our methodology.
Practical implications: The network pruning methods we proposed will promote the analysis of the ICIO network, in terms of community detection, link prediction, and spatial econometrics, etc. Also, they can be applied to many other complex networks with similar characteristics.
Originality/value: This paper improves the existing research from two aspects, namely, considering the heterogeneity of weights and avoiding the interference of parameters. Therefore, it provides a new idea for the research of network backbone extraction.