网络动力学视角下人工智能合作创新网络演化机制

吕建林 ,  尹西明 ,  胡斌

昆明理工大学学报(自然科学版) ›› 2026, Vol. 51 ›› Issue (3) : 188 -200.

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昆明理工大学学报(自然科学版) ›› 2026, Vol. 51 ›› Issue (3) : 188 -200. DOI: 10.16112/j.cnki.53-1223/n.202602040005
数理科学与管理工程

网络动力学视角下人工智能合作创新网络演化机制

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Evolutionary Mechanisms of Collaborative Innovation Networks in Artificial Intelligence from a Network Dynamics Perspective

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摘要

知识经济背景下,合作网络已成为人工智能领域重要创新渠道.本文基于复杂适应系统理论,利用2010—2024年人工智能领域联合授权发明专利构建合作创新网络,在网络动力学视角下,借助随机行动者导向模型从内生结构、外源属性与多维邻近特征深入探究其对合作创新网络演化的差异性影响机制,并剖析内外因素交互效应.研究表明:密度对网络演化具有负向影响,传递三元组和流行度则发挥正向影响.创新积累通过吸引合作关系与主动探索新关系的双重路径来驱动网络演化,创新积累差异较大的组织间更容易建立合作关系.地理和文化邻近对网络演化具有正向影响,组织和技术邻近则发挥负向影响.网络演化还受到内外因素的复杂交互效应影响.本研究突破创新网络演化的单维解释局限,为优化人工智能创新网络提供关键理论依据.

Abstract

In the context of the knowledge economy,collaboration networks have become a vital channel for innovation in the field of artificial intelligence (AI).Based on complex adaptive system theory,this paper uses jointly authorized invention patents in the field of AI from 2010 to 2024 to construct collaborative innovation networks.From the perspective of network dynamics,this paper uses stochastic actor-oriented models to explore the differential impact mechanisms of endogenous structure,exogenous attributes,and multidimensional proximity characteristics on the evolution of collaborative innovation networks and analyzes the interactive effects of internal and external factors.The results show that density has a negative impact on network evolution,while transitive triplets and popularity have a positive impact.Innovation accumulation drives network evolution through the dual paths of attracting partnerships and actively exploring new ties.Organizations with greater differences in innovation accumulation are more likely to establish collaborative ties.Geographical and cultural proximity have a positive impact on network evolution,while organizational and technological proximity have a negative impact.Network evolution is also influenced by the complex interplay of internal and external factors.This study breaks through the limitations of one-dimensional interpretations of innovation network evolution and provides a key theoretical basis for optimizing AI innovation networks.

关键词

人工智能(AI) / 合作创新网络 / 多维邻近 / 网络动力学 / 网络演化机制

Key words

artificial intelligence / collaborative innovation network / multidimensional proximity / network dynamics / network evolution mechanism

引用本文

引用格式 ▾
吕建林,尹西明,胡斌. 网络动力学视角下人工智能合作创新网络演化机制[J]. 昆明理工大学学报(自然科学版), 2026, 51(3): 188-200 DOI:10.16112/j.cnki.53-1223/n.202602040005

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基金资助

国家自然科学基金面上项目(72474025)

工业和信息化部重大课题(GXZK2025-107)

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