Structure Of Innovation Networks Research Articles

The effects of proximities on the evolving structure of intercity innovation networks in the Guangdong–Hong Kong–Macao Greater Bay Area: comparison between scientific and technology knowledge

ABSTRACT The various knowledge flows shape and change the regional innovation patterns, which are also influenced by regional conditions. What are the similarities, differences and connections between science and technology linkage, as two different types of knowledge network, deserve to be explored in depth. Drawing on scientific paper co-publications and patent transfer data, we constructed two different types of intercity innovation networks during 2006–2016 in the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), a city region special for its ‘one country, two systems’ structure. After that, we explored the evolutionary characteristics of the networks and further examined the different impacts of multidimensional proximity on scientific collaboration and technology transfer. Our results show that technology transfer is more sensitive to spatial factors, institutional barriers caused by ‘one country, two systems’ is a bigger obstacle to technology transfer between cities, cultural proximity and cognitive proximity have a more significant impact on paper cooperation network. Moreover, geographical proximity can indirectly affect knowledge spillover by acting on the proximity of other dimensions. As for scientific collaboration, social, cognitive and institutional proximities can compensate for the lack of geographical proximity, and cultural proximity frequently goes along with geographical proximity; as for technology transfer, geographical proximity has neither substitutional or complementarity relations with cultural and cognitive proximities, the interrelatedness between geographical and institutional and social proximities are complementarity which is opposed to paper co-publications. This study explores the differences in spillover mechanisms of different knowledge types and contributes to enriching the empirical framework of multidimensional proximities and innovation network researches.

KIEGLFN: A unified acne grading framework on face images

Background and Objective: Grading the severity level is an extremely important procedure for correct diagnoses and personalized treatment schemes for acne. However, the acne grading criteria are not unified in the medical field. This work aims to develop an acne diagnosis system that can be generalized to various criteria. Methods: A unified acne grading framework that can be generalized to apply referring to different grading criteria is developed. It imitates the global estimation of the dermatologist diagnosis in two steps. First, an adaptive image preprocessing method effectively filters meaningless information and enhances key information. Next, an innovative network structure fuses global deep features with local features to simulate the dermatologists’ comparison of local skin and global observation. In addition, a transfer fine-tuning strategy is proposed to transfer prior knowledge on one criterion to another criterion, which effectively improves the framework performance in case of insufficient data. Results: The Preprocessing method effectively filters meaningless areas and improves the performance of downstream models.The framework reaches accuracies of 84.52% and 59.35% on two datasets separately. Conclusions: The application of the framework on acne grading exceeds the state-of-the-art method by 1.71%, reaches the diagnostic level of a professional dermatologist and the transfer fine-tuning strategy improves the accuracy of 6.5% on the small data.

Research on Cooperative Innovation Network Structure and Evolution Characteristics of Electric Vehicle Industry

The electric vehicle industry involves various technological cooperative innovations, meaning that electric vehicle companies must not only cooperate and innovate with similar enterprises in the same industry, but also collaborate with enterprises across the industry and research institutions (RIs). This paper empirically analyzed the structure and evolution characteristics of cooperative innovation networks, based on the patent application data in the field of electric vehicles from 2006 to 2021. It was found that the firm–firm intra-industry and inter-industry cooperative innovation networks, and firm–RI cooperative innovation networks have the characteristics of phased evolution in structure, and the network structure in the evolution process has similarities and differences. Furthermore, during the industrial formation period, inter-industry cooperative innovation focuses on the cooperation between the midstream and downstream industries of the industrial chain; but in the industrial growth period, inter-industry cooperative innovation has been widely extended to multiple industries in the upper, middle and lower reaches of the industrial chain. While the scale of intra-industry and industry–research institution cooperative innovation continues to expand with the development of the industry, the collaboration is concentrated in the five industries in the middle and downstream of the industrial chain. The research conclusion can provide a reference for different cooperative innovation partners of electric vehicles and other emerging industries to formulate differentiated policies.

Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China

Understanding the evolutionary characteristics of innovation network structure can improve urban innovation and regional construction. Urban innovative development is affected by various factors, which can be analyzed via models of innovation networks. We establish a multi-criteria evaluation system of innovation capability and use an improved gravity model to construct an innovation network for 2015–2018, employing social network methods to analyze structural characteristics and spatial patterns. Results show that: (1) The innovation of cities in the urban agglomeration in the middle reaches of the Yangtze River has gradually increased, with an accompanying increase in the complexity of innovation networks. The cities of Wuhan, Changsha, and Nanchang are located at the absolute core of this network, which exhibits a Matthew effect, and has a triangle integration mode of growth. (2) The attraction of innovative resources and the promotion of individual innovation are increasing every year within the cities. The aggregation pattern of innovation shows a multi-core state in the urban agglomeration in the middle reaches of the Yangtze River, but the innovation radiation pattern has changed from a single center to a double center. (3) Multiple spatial innovation axes are seen in the network, with a location and direction consistent with the urban agglomeration’s development axis in the Yangtze River’s middle reaches and a triangle integration growth mode. Policy implications are proposed for regional innovation and development, and our results can provide future policy guidance and direction for governmental entities and other stakeholders.

Research on the Influence of Collaborative Innovation Network Structure and Innovation Capability in the Yangtze River Economic Belt

This paper uses the modified gravity model to establish the collaborative innovation network among 110 cities in the Yangtze River Economic Belt from 2009 to 2018, and uses social network analysis and dynamic panel model to study the impact of innovation network structure on the level of collaborative innovation. The results show that: (1) The collaborative innovation network connection level of the Yangtze River Economic Belt is relatively low, showing a spatial distribution pattern of “high in the east and low in the west”, and the degree of network connection is not high, and the network structure is relatively loose. (2) The power of network structure significantly promotes the improvement of the level of collaborative innovation, but the improvement effect has certain differences, which is embodied in the promotion effect of proximity centrality is greater than that of degree centrality is greater than that of intermediate centrality. The degree of influence of network structure power varies.

The Network Structure of Innovation Networks

The Network Structure of Innovation Networks

Evolution of the emergency drug innovation network and policy implications: Evidence from COVID-19 drug patents

Abstract Developing emergency drugs in time is key to responding to public health emergencies such as the coronavirus disease 2019 (COVID-19) pandemic. This research constructs patent assignee citation networks based on COVID-19 drug patents and applies technology life cycle analysis and social network analysis to examine these networks. The results reveal the evolution and structure of the emergency drug innovation network and identify innovation entities with different research and development (R&D) capabilities or a foundation for cooperation. This study attempts to provide the basis for theories and policies to accelerate R&D of emergency drugs. The key results are as follows: (1) Technologies of the emergency drug innovation network show fluctuating growth aligned closely with public health emergencies. (2) The network presents a sparse structure with many active factions, whose members have the advantage of cooperating. (3) Three types of innovation entities are identified: the main technology suppliers, potential suppliers of other supporting technologies, and peripheral suppliers of other supporting technologies.

The Knowledge Based View of Embedding Strategy for Platforms Enterprises

Dialectically linking with heterogenous suppliers, manufacturers, consumers and resources, emerging e-commerce platforms achieve and maintain competitive advantage. Based on different levels of innovation network content and structure, YEATION and YOUPIN, two typical ecommerce platforms, adopt different strategies involving the promotion of value-seeking. Many decisions concern how companies perceive the innovation environment and how to engage in the innovation network. Knowledge base view provides useful theoretical lens to understand the network embedding approach for electric business platform enterprises. To put forward, enterprises with wide breadth should adopt structural strategy give priority to relational embeddedness, on the other hand, enterprises of which the depth of knowledge base is deep and width of insufficient shall adopt strategy give priority to the embedding of structure. It is also proposed that platform enterprises should not adopt a single embeddedness strategy but should adopt auxiliary embeddedness strategy in a timely manner.

Spatial Pattern and Evolution of Global Innovation Network from 2000 to 2019: Global Patent Dataset Perspective

In the era of the knowledge economy, the improvement of national innovation systems is playing a significant role in the global entrepreneurship ecosystem. Entrepreneurs are accelerating international intellectual property applications to be competitive. What remains to be explored is the evolution of international intellectual property network in the globe. With the application of social network analysis and intellectual property application database, the global innovation network structure from 2000 to 2019 is explored. Results showed that (1) in the period 2000–2019, the global innovation network has been expanding rapidly from a sparse network to a dense and complex one. (2) Patent application is unevenly distributed in the globe. Countries such as the US, China, and Canada have been the top countries flowing in, while Japan, Korea, EU, and Switzerland have been the main countries flowing out. (3) Global innovation network shows an obvious “core‐periphery” pattern. The distribution pattern presents a quadrilateral structure with the four core regions of “US, Japan, EU, and China” as the apex. This analysis contributes to the visualization of the global layout of intellectual property and the evolution trend by analyzing intellectual property application networks. This can provide important experience reference for enterprises to study the global entrepreneurship ecosystem.

Spatial competition, strategic R&D and the structure of innovation networks

We provide a network game to analyze the innovation network structure by incorporating endogenous R&D spillovers and diffusion effects, reconciling the limited types of innovation alliance in practice. In our network model, enterprises can actively choose to invest in R&D or establish links with others to absorb R&D spillovers. We characterize the network structure of enterprises' strategic R&D decisions in different types of spatial equilibrium. We further extend the theoretical framework to generate industry-specific implications under cooperative R&D, location choice, spatial agglomeration, and social capital allocation in innovation networks.

The Impact of the Preferential Attachment Level on the Innovation Network Structure and Innovation Efficiency

In the formation process of innovation networks, the level of preferential attachment between agents has an important impact on the resulting structural and functional characteristics of a network. A simulation model of innovation networks with variable preferential levels of attachment is established to study these impacts. Negative preferences tend to form chain-type structures, while positive preferences tend to form star-type structures. Furthermore, a simulation process of innovation is added to the network model to test the impact of the resulting structures on innovation efficiency. For explorative innovation, the structure with unbiased preference has the fastest innovation speed, but the structures with strong positive preference show lower innovation costs. For exploitive innovation, strong negative preferences generate the fastest innovation speed and relatively low cost, while strong positive preferences show a slightly lower cost than negative preference structures but a much lower innovation speed. Finally, some internal mechanisms of the results are discussed.

China's rare earth industry technological innovation structure and driving factors: A social network analysis based on patents

Based on patent information, the paper analyzes the technological comparative advantages from the perspective of industrial chain, and adopts the Social Network Analysis (SNA) method and knowledge production function model to explore technological innovation network structure of China's rare earth industry and its driving force. The results show that, overall, China's rare earth industry technological innovation ability has continued to increase in terms of the breadth and depth, and the industrial chain has shown the characteristics of “strong upper and weaker lower”. The network structure analysis show that the overall technical connection network tends to be close and stable, but it is still in a state of weak connection. In terms of individual networks, technologies represented by the development, extraction and smelting of rare earth resources constitute the core participants in the innovation network, and play the role of the power and control center of the technological innovation network. Further factors identification shows that China's innovation environment is the biggest driving force for technological innovation in the rare earth industry, followed by R&D investment and resource retention capacity. Additionally, the development of strategic emerging industries related to rare earths and resource production capacity have obvious lag effect on the technological innovation level.

RETRACTED ARTICLE: Agglomeration of technology innovation network of new energy automobile industry based on IoT and artificial intelligence

We have entered the era of big data. Traditional B2C monitoring platforms are far from being able to meet the needs of existing users, and transmission costs will create invisible pressure. Data are the intangible assets of new energy vehicle companies. With the increase in user usage and the accumulation of data, more and more people have begun to discuss how to monitor the operation of data to provide effective data for operators and end users of new energy vehicles. From the perspective of cluster technology, the impact of industrial innovation and various aspects of industrial innovation are explained in detail. The technological innovation process of clusters and industrial clusters is complex system engineering. Under the conditions of the result of interaction, various elements of industrial clusters are formed in a specific social and economic environment to promote interaction. Second, from saving transaction costs to facing the internal mechanism of industrial cluster technological innovation, theoretically analyze the industrial value chain of knowledge sharing, supplementary assets and industrial cluster knowledge, and then analyze from the industrial cluster innovation. The technological innovation mechanism in this paper is the network structure, application mechanism and functional mechanism of the industrial cluster innovation network. Based on game theory, through the establishment of a microeconomic model to analyze the internal mechanism of the ripple effect and integration effect under technological innovation, and verify it by measuring the ripple effect, integration effect and technological innovation of the internal mechanism.

How Could Policies Facilitate Digital Transformation of Innovation Ecosystem: A Multiagent Model

The digital transformation of the innovation ecosystem is not only an inevitable direction of innovation activities in the era of digital economy but also a highly complex and uncertain process. The way to facilitate transformation with policies has become a topic of common concern of academia and policymakers. This paper builds a multiagent model and studies the impacts of supply-side policies, demand-side policies, and environmental policies on enterprises’ transformation willingness, digital level, and income level as well as the proportion of enterprises that carry out transformation in the whole innovation ecosystem and innovation network structure by numerical experiments. According to research findings, supply-side policies play the biggest role in the facilitation of transformation, demand-side policies are second important to them, and environmental policies have comparatively weak impacts.

Evolution Mechanism of Advanced Equipment Manufacturing Innovation Network Structure from the Perspective of Complex System

Our country’s equipment manufacturing industry ranks among the best in all developing countries, but compared with developed countries, there is still a long way to go. It is not only the backwardness of various technologies, but also the interference of other countries. Although our country's equipment manufacturing industry is not as advanced as the advanced technology of developed countries, we still have to stick to our original aspirations, do not underestimate ourselves, and be good at absorbing and learning from the strengths of others to make up for our own weaknesses. While not working behind closed doors and while absorbing technology from other countries, we can make use of our strengths to make up for our weaknesses and develop our own industrial technology. This paper studies the evolution trend of innovation network structure and at the same time studies the evolution mechanism of advanced equipment manufacturing innovation network structure from the perspective of complex systems. The explained variable in this article is green total factor productivity. The variable adopts the Malmquist–Luenberger global super-efficiency index model. There are two main explanatory variables. One is the heterogeneity that affects the efficiency of industrial evolution, including factor heterogeneity, structural heterogeneity, and environmental heterogeneity, and the other is the interaction term of equipment manufacturing specialization agglomeration degree dummy variable multiplied by factor heterogeneity. The regional economic development level is added to the model as a control variable. In the selection of measurement indicators, the per capita GDP is used as the control variable. The experimental results show that each sample is tested in pairs, and the standard error level of the mean is 0.018, which is less than 0.05, indicating that the efficiency of the equipment manufacturing industry’s economic correlation spatial network has a significant impact on the overall economic development level of the industry. The reduction in spur helps to increase economic output.

Research on Collaborative Innovation Network Mechanism of General Aviation Enterprises Based on Complex Network

In order to better promote the collaborative innovation ability among general aviation enterprises and promote the high-quality development of general aviation industry, this paper is based on the social network relationship theory and embeddedness principles, using the general aviation enterprises as structural embedding to determine the key nodes of the innovation network. The value chain, supply and demand chain, and information sharing behavior are used as relational embeddings to build a general aviation enterprise chain innovation network. On this basis, from the aspects of network topology graph, node degree, clustering coefficient, etc., quantitative analysis of the structure and characteristics of the general aviation enterprise innovation network is carried out, so as to lay a foundation for the functional orientation of the main body of collaborative innovation in the aviation industry. The research shows that: At present, the collaborative innovation of general aviation enterprises has not formed a scale. The inn ovation of enterprises mainly relies on large-scale general aviation enterprises with stat e prefix. Most general aviation enterprises have weak innovation capabilities, and the s mall and medium-sized general aviation enterprises are in the initial development stage. Therefore, it is necessary to strengthen the support of various types of general aviation enterprises, provide support in terms of funds and policies and so on, so as to promote the improvement of collaborative innovation capabilities among various types of general aviation enterprises

Innovation network characteristics and equipment manufacturing industry upgrading: based on the mediating effect of innovation ability

In order to strengthen the guiding and supporting role of the innovation-driven development strategy, and to promote the structural optimization and efficiency improvement of the equipment manufacturing industry, this paper analyzes the structure, function and characteristics of the regional equipment manufacturing industry innovation network, and proposes a conceptual model and theoretical hypothesis that the innovation network based on the mediating effect of innovation capability affects the upgrade effect of the regional equipment manufacturing industry. Through a questionnaire survey of 219 enterprises in my country’s equipment manufacturing industry and structural equation model verification, it measures the action path and industrial upgrading law of innovation network influencing regional equipment manufacturing industry upgrading.

The Embedded Strategy of Enterprise Innovation Network Based on Knowledge Base

Constructing the innovation network is an important means of breakthrough in the traditional manufacturers for the emerging e-commerce platform, based on two different levels of innovation network node, content and structure, YEATION and YOUPIN were chosen to analyse and research the relationship and between knowledge base and the embedding strategy of electric business platform enterprises to create innovation network. To put forward the knowledge base width wider, insufficient depth of the enterprise should adopt structural strategy give priority to relational embeddedness, on the other hand, enterprises of which the depth of knowledge base is deep and width of insufficient shall adopt strategy give priority to the embedding of structure. At the same time, it is proposed that platform enterprises should not adopt a single embeddedness strategy, but should adopt auxiliary embeddedness strategy in a timely manner.

How Do Innovation Network Structures Affect Knowledge Sharing? A Simulation Analysis of Complex Networks

Knowledge has become an important resource that can help enterprises gain a competitive advantage in the market. In this regard, knowledge sharing in the process of cooperative innovation provides an important way to acquire knowledge. With the development of innovation, the innovation network has become an important carrier of knowledge sharing, which can also have an influence on knowledge sharing. Based on knowledge management theory and complex network theory, this study constructed a multilayer network environment for knowledge sharing. It then identified the network elements and knowledge‐sharing paths that influence knowledge sharing from microperspectives and macroperspectives. On this basis, the effects of node cohesion and weak connection on knowledge sharing in small‐world and scale‐free topologies were analyzed by computer simulation. The results showed that, in an innovation network with a scale‐free topology, cohesion and weak ties had a great influence on the average knowledge level and knowledge equilibrium. Meanwhile, in small‐world topological innovation networks, a mixed path had the best promotion effect on network knowledge sharing.

Random convolutional neural network structure: An intelligent health monitoring scheme for diesel engines

Automatic and accurate identification on the health condition of the diesel engine is a challenging task in the modern industry. In this paper, an innovative deep learning network structure called Random Convolutional Neural Network (RCNN) is designed for the intelligent health monitoring of diesel engines, taking full advantages of deep learning and ensemble learning. Firstly, this novel network framework is constructed with several individual convolutional neural networks (CNN), which can automatically extract the discriminative features of vibration signals by convolutional calculation and pooling operation. Secondly, an improved optimizer called Adabound and the Dropout technique are adopted in the framework of RCNN. The Adabound optimizer uses adaptive learning rates to accelerate the training of network and avoid plunging into local optimum. Finally, a combinational rule is used to fuse the diagnostic results from several individual CNN. The experimental vibration signals acquired from the diesel engine prove that the efficiency and superiority of the proposed RCNN.