Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective

Xu, Gening; Dong, Chen; Meng, Lei

doi:10.3390/su142114002

Cited by 3 publications

(1 citation statement)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They predicted potential competing partners by analyzing the centrality of nodes and link predictions in patent cooperation networks. Xu et al [31] constructed an AI collaborative innovation network and an AI patent cooperation network, based on nearly ten years of patent cooperation data in the field of AI in the Yangtze River Delta region of China. They explored the basic features and spatio-temporal evolution of these two networks, and predicted the future partnership between cities and innovation entities.…”

Section: Literature Reviewmentioning

confidence: 99%

Drifting toward Alliance Innovation: Patent Collaboration Relationships and Development in China’s Hydrogen Energy Industry from a Network Perspective

Pan,

Xu,

Meng

2024

Sustainability

Self Cite

View full text Add to dashboard Cite

The hydrogen energy industry, as one of the most important directions for future energy transformation, can promote the sustainable development of the global economy and of society. China has raised the development of hydrogen energy to a strategic position. Based on the patent data in the past two decades, this study investigates the collaborative innovation relationships in China’s hydrogen energy field using complex network theory. Firstly, patent data filed between 2003 and 2023 are analyzed and compared in terms of time, geography, and institutional and technological dimensions. Subsequently, a patent collaborative innovation network is constructed to explore the fundamental characteristics and evolutionary patterns over five stages. Furthermore, centrality measures and community detection algorithms are utilized to identify core entities and innovation alliances within the network, which reveal that China’s hydrogen energy industry is drifting toward alliance innovation. The study results show the following: (1) the network has grown rapidly in size and scope over the last two decades and evolved from the initial stage to the multi-center stage, before forming innovation alliances; (2) core innovative entities are important supports and bridges for China’s hydrogen energy industry, and control most resources and maintain the robustness of the whole network; (3) innovation alliances reveal the closeness of the collaborative relationships between innovative entities and the potential landscape of China’s hydrogen energy industry; and (4) most of the innovation alliances cooperate only on a narrow range of technologies, which may hinder the overall sustainable growth of the hydrogen energy industry. Thereafter, some suggestions are put forward from the perspective of an industrial chain and innovation chain, which may provide a theoretical reference for collaborative innovation and the future development and planning in the field of hydrogen energy in China.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Drifting toward Alliance Innovation: Patent Collaboration Relationships and Development in China’s Hydrogen Energy Industry from a Network Perspective

Pan,

Xu,

Meng

2024

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

Identification of Network Topology Changes Based on r-Power Adjacency Matrix Entropy

Dong,

2023

J Stat Phys

View full text Add to dashboard Cite

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis

Qi,

Zhu,

et al. 2024

J Med Internet Res

View full text Add to dashboard Cite

Background With the rise of artificial intelligence (AI) in the field of dementia biomarker research, exploring its current developmental trends and research focuses has become increasingly important. This study, using literature data mining, analyzes and assesses the key contributions and development scale of AI in dementia biomarker research. Objective The aim of this study was to comprehensively evaluate the current state, hot topics, and future trends of AI in dementia biomarker research globally. Methods This study thoroughly analyzed the literature in the application of AI to dementia biomarkers across various dimensions, such as publication volume, authors, institutions, journals, and countries, based on the Web of Science Core Collection. In addition, scales, trends, and potential connections between AI and biomarkers were extracted and deeply analyzed through multiple expert panels. Results To date, the field includes 1070 publications across 362 journals, involving 74 countries and 1793 major research institutions, with a total of 6455 researchers. Notably, 69.41% (994/1432) of the researchers ceased their studies before 2019. The most prevalent algorithms used are support vector machines, random forests, and neural networks. Current research frequently focuses on biomarkers such as imaging biomarkers, cerebrospinal fluid biomarkers, genetic biomarkers, and blood biomarkers. Recent advances have highlighted significant discoveries in biomarkers related to imaging, genetics, and blood, with growth in studies on digital and ophthalmic biomarkers. Conclusions The field is currently in a phase of stable development, receiving widespread attention from numerous countries, institutions, and researchers worldwide. Despite this, stable clusters of collaborative research have yet to be established, and there is a pressing need to enhance interdisciplinary collaboration. Algorithm development has shown prominence, especially the application of support vector machines and neural networks in imaging studies. Looking forward, newly discovered biomarkers are expected to undergo further validation, and new types, such as digital biomarkers, will garner increased research interest and attention.

show abstract

Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective

Cited by 3 publications

References 59 publications

Drifting toward Alliance Innovation: Patent Collaboration Relationships and Development in China’s Hydrogen Energy Industry from a Network Perspective

Drifting toward Alliance Innovation: Patent Collaboration Relationships and Development in China’s Hydrogen Energy Industry from a Network Perspective

Identification of Network Topology Changes Based on r-Power Adjacency Matrix Entropy

Mapping Knowledge Landscapes and Emerging Trends in AI for Dementia Biomarkers: Bibliometric and Visualization Analysis

Contact Info

Product

Resources

About