Multi-relational Network Identification in Cross-fusion Domains of Information-technology-related Industries

LI Baoxiang; KONG Lingkai; YANG Xiaomeng

doi:10.13306/j.1672-3813.2026.04.010

Complex Systems and Complexity Science >

2026 , Vol. 23 >Issue 4: 82 - 90

DOI: https://doi.org/10.13306/j.1672-3813.2026.04.010

Complex Network

Multi-relational Network Identification in Cross-fusion Domains of Information-technology-related Industries

LI Baoxiang ,
KONG Lingkai ,
YANG Xiaomeng

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1. School of Economics and Management, Xiamen University of Technology, Xiamen 361024, China;
2. Faculty of Management and Economics, Kunming University of Science and Technology, Kunming 650504, China;
3. School of Economics and Management, Jiangsu University of Science and Technology, Zhenjiang 215699, China

Online published: 2026-09-15

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Abstract

In order to identify and analyze the multi-relational network of cross-industry convergence fields, we used the LDA topic model, and applied the topic intensity, text similarity, co-presence rate, co-presence network analysis and the complex network analysis method to identify the multi-relational network. The findings reveal the evolutionary characteristics of multi-relational network topology, maximum communities, and key nodes. The findings indicate that universities have consistently occupied key node positions within these networks and have maintained a dominant control over network resources. Enterprises have gradually taken on more significant node positions in these networks. Furthermore, the core-periphery structures and the phenomenon of enterprises clustering together have become increasingly evident. This research provides a new approach for uncovering the associations and growth patterns among heterogeneous-relation agents in complex networks. It offers empirical evidence for enterprises seeking to acquire technological resources and gain a competitive edge in the market, while also serving as a valuable reference for government departments aiming to optimize policies for industrial convergence and development.

Key words： LDA model; cross-fusion domain; multi-relational network; network identification and analysis

Cite this article

LI Baoxiang , KONG Lingkai , YANG Xiaomeng . Multi-relational Network Identification in Cross-fusion Domains of Information-technology-related Industries[J]. Complex Systems and Complexity Science, 2026 , 23(4) : 82 -90 . DOI: 10.13306/j.1672-3813.2026.04.010

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