Examining the evolving structures of intercity knowledge networks: the case of scientific collaboration in China

Scientific research collaboration networks are well-established research topics, which can be divided into two kinds of research paradigms: (1) The topological features of the whole scientific collaboration networks and the collaboration representations in some given fields. (2) The individual nodes' characteristics in the collaboration networks and their endorsements in the networks. However, in the above studies, all the nodes' roles in the scientific collaboration network are the same, all of whom are called collaborators, thus the relationships among all the nodes in the scientific collaboration network are symmetric, and the scientific collaboration network is undirected. Such symmetric roles and relationships in the undirected networks have no incentive effects on the members' participations and efforts in the team's scientific research. In this paper, the roles of team members in the scientific research collaborations are defined, including the scientific research pioneers and contributors, their collaboration relationships are considered from the viewpoint of principal-agent theory, and then the directed scientific collaboration network is built. Then the benefit distribution mechanism in the team members' networked scientific research collaborations is presented, which will encourage the team members with different roles to make their efforts in their scientific research collaborations and improve the quality of scientific research outputs. An example is used to test the above ideas and conclude that the individual member's real outputs not only lie in his/her real scientific research efforts, but also rest with his/her contributions to other members' scientific research.

As a crucial vehicle for green technological innovation, cooperative networks significantly promote resource integration and knowledge sharing. Yet, their dynamic evolution and micro-mechanism remain underexplored. Drawing on data from the joint applications of green invention patents between 2006 and 2021, this study constructed a multi-agent GTCIN involving multiple stakeholders, such as enterprises, universities, and research institutions, and analyzed the topological structure and evolutionary characteristics of this network; an exponential random graph model (ERGM) was introduced to elucidate its endogenous and exogenous driving mechanisms. The results indicate that while innovation connections increased significantly, the connection density decreased. The network evolved from a “loose homogeneity” to “core aggregation” and then to “outward diffusion”. State-owned enterprises in the power industry and well-known universities are located at the core of the network. Preferential attachment and transitive closure as endogenous mechanisms exert strong and continuous positive effects by reinforcing local clustering and cumulative growth. The effects of exogenous forces exhibit stage-specific characteristics. State ownership and regional location become significant positive drivers only in the mid-to-late stages. The impact of green innovation capability is nonlinear, initially promoting but later exhibiting a significant inhibitory effect. In contrast, green knowledge diversity exerts an opposite pattern, having a negative effect in the early stage due to integration difficulties that turns positive as technical standards mature. Geographical, technological, social, and institutional proximity all have a positive promoting effect on network evolution, with technological proximity being the most influential. However, organizational proximity exerts a significant inhibitory effect in the later stages of GTCIN evolution. This study reveals the shifting influence of endogenous and exogenous mechanisms across different evolutionary phases, providing theoretical and empirical insights into the formation and development of green innovation networks.

Multidimensional proximity has been considered the important factor affecting regional scientific collaboration network (RSCN) and has received extensive attention from scholars. This paper constructs a cross-RSCN based on co-authorship across 31 regions in China, incorporates traditional multidimensional proximity and network embedding proximity into a unified framework and proposes a five-factor driving model for dissecting the mechanism of cross-regional scientific collaborative innovation (CI). The results show that the scientific collaboration network is gradually becoming saturated with connections, and the polarisation of cross-regional collaboration is increasingly serious. The collaboration intensity is affected by the co-movement of several proximity factors, among which geographic proximity (GP) is the main but not the sole one that positively affects the cross-regional scientific collaboration. Based on the decision tree model, we conclude that structure proximity may compensate for low GP and low economic proximity (EP) on cross-regional CI performance. The positive effect of network location proximity on regional scientific collaboration is more sensitive when the EP is not high. The findings provide significant guidance for scientific research institutions, universities and regional policy-makers to select appropriate partners as well as network embedding strategies, and further contribute to long-term improvement of scientific innovation performance and stable regional economic growth.

Scientific collaboration networks have been studied systematically since 1960 by scholars belonging to various disciplinary backgrounds. As a result, the complex phenomenon of scientific collaboration networks has been investigated within different approaches. Although the term “scientific collaboration network” has different connotations in the literature, we use the term more narrowly to focus on scientific collaboration resulting in co-authored public documents. We broaden this beyond journal articles to include many types of scientific productions in addition to journal articles and books. We insist that these productions are public items available in each field. In this chapter, we focus on the main quantitative approaches dealing with the structure and dynamics of scientific collaboration networks through co-authorized publications. We provide a brief history of social network analysis that serves as a foundation. We further review earlier conceptual classifications of co-authorship networks and distinguish cross-disciplinarily, cross-sectoral and cross-national levels. We couple the newer ideas of “small world” models and “preferential attachment” to older sociological conceptions of scientific collaboration. This is followed by descriptions of deterministic and stochastic models that have been used to study dynamic scientific collaboration networks. We stress the importance of delineating the topology of collaboration networks, understanding micro-level processes and then coupling them. We conclude by outlining the strengths and limitations of various modeling strategies.KeywordsRandom GraphSocial Network AnalysisPreferential AttachmentCitation NetworkScientific CollaborationThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Modeling study of knowledge diffusion in scientific collaboration networks based on differential dynamics: A case study in graphene field

Background:Social network analysis (SNA) evaluates the connections and behavior of individuals in social groups. The scientific collaboration network is a kind of SNAs. A social network could be defined as a collection of nodes (social existence) and links (connections) associated with the nodes. The aim of this study was to evaluate the scientific outputs and collaboration networks of the countries and authors using indicators of SNA in the field of pituitary disorders between 2000 and 2020.Methods:This is a practical study performed by applying a scientometric approach and SNA. We retrieved 31257 papers in the field of pituitary disorders between 2000 and 2020. Data were analyzed using scientific software, namely, VOSviewer, UciNet, and Netdarw.Results:Based on degree centrality, Colao and Pivonello in the world, Shimon and Kadioghlu in the Middle-East (ME), and Khamseh, Ghorbani in Iran achieved the top ranking. Based on the betweenness centrality, Pivonello, Colao, and Chanson in the world, Laws, and Kadioghlu in the Middle-East, and Larijani, Mohseni, and Khamseh in Iran were known as the top authors. According to closeness centrality, Pivonello, Colao, and Chanson in the world, Kadioghlu and Kelestimur in the Middle-East, and Mohseni, Khamseh, and Larijani in Iran were the top authors. The map of the authors’ collaboration in the field of pituitary disorders consists of 92 nodes. A total number of 77313 authors had global collaboration. The global collaboration network was comprised of 129 nodes (country) and 2694 links (country’s collaboration). The Middle-East collaboration network revealed 69 nodes and 1708 links. The collaboration network of the Middle-East countries consists of 13 nodes and 50 links. Conclusion:Authors with a higher degree, betweenness and closeness centrality have greater efficiency (the number of articles) and effectiveness (the number of received citations). Moreover, the authors and countries that published more scientific products received more citations. In addition, in the Middle-East countries, the interdisciplinary scientific collaboration between the researchers in the fields of endocrinology, neurosurgery, pathology, and radiology has a significant impact on improving scientific outputs.

Scientific knowledge is an underlying basis for technological innovation in the pharmaceutical industry. Collaboration is the main way to participate in the creation of scientific knowledge for pharmaceutical firms. Will network positions in scientific collaboration affect their technological innovation performance? Moreover, what factors moderate the firms' scientific collaboration network positions and technological innovation link? Using a dataset based on 194 Chinese publicly traded pharmaceutical companies, this paper constructs the dynamic scientific collaboration networks among 1,826 organizations by analyzing 4,092 papers included in CNKI and Web of Science databases. Then we probe the impact and boundaries of positions in the scientific collaboration network of pharmaceutical firms on their technological innovation performance through the negative binomial modeling approach. Our study confirms that degree centrality has an inverted U-shaped impact on pharmaceutical firms' technological innovation performance, while structural holes benefit it. Moreover, this article identifies that the strength of scientific collaboration positively moderates the U-shaped relationship between degree centrality and technological innovation of pharmaceutical firms, the matching of high patent stock and high structural holes can promote their technological innovation performance. The results deepen the present understanding of scientific collaboration in the pharmaceutical industry and offer new insights into the formulation of pharmaceutical firms' scientific collaboration strategies.

Scientific collaboration is essential in solving problems and breeding innovation. Coauthor network analysis has been utilized to study scholars' collaborations for a long time, but these studies have not simultaneously taken different collaboration features into consideration. In this paper, we present a systematic approach to analyze the differences in possibilities that two authors will cooperate as seen from the effects of homophily, transitivity, and preferential attachment. Exponential random graph models (ERGMs) are applied in this research. We find that different types of publications one author has written play diverse roles in his/her collaborations. An author's tendency to form new collaborations with her/his coauthors' collaborators is strong, where the more coauthors one author had before, the more new collaborators he/she will attract. We demonstrate that considering the authors' attributes and homophily effects as well as the transitivity and preferential attachment effects of the coauthorship network in which they are embedded helps us gain a comprehensive understanding of scientific collaboration.

In efforts to address the far-reaching effects of climate change and associated impacts in communities, research on environmental philanthropy suggests that more resources are being allocated to environmental societal challenges. However, understandings about which environmental nonprofits benefit from these funding flows is limited. This study integrates resource dependency theory with elitism and pluralism perspectives to analyze a network of environmental nonprofits and their funders in Texas. Resource dependency and a network-analytic approaches share underlying relationality principles, and we connect those dots by conceptualizing the funding dynamics in Texas as a network of funder-grantee relations. Drawing on statewide survey data (n = 114), we use a network analysis technique – exponential random graph modeling (ERGM) – to analyze funding allocations in Texas through the organizational attributes of environmental nonprofits, their funder-grantee relations, and their community context. We specifically observe elitism in funding allocations in Texas, which is evident in network effects (preferential attachment) and the focal areas of environmental work. However, we find limited evidence that age or resources of the nonprofit are predictive of funding and the most influential factor determining a funder-grantee relationship is the natural hazard risk of the community served by the nonprofit. Our findings suggest interconnected funding dynamics of pluralism and elitism in the Texas environmental philanthropy landscape, prompting further discussion about the potential synergies of these patterns and the implications for environmental funding practices.

This paper examines the emergence of China – now the world’s largest source of scientific publications – in global science from the perspective of the connectivity of its major cities in interurban scientific collaboration networks. We construct collaboration networks between 526 major cities (including 44 Chinese cities) for 2002–2006 and 2014–2018 based on co-publication data drawn from the Web of Science. Both datasets are analyzed using a combination of different centrality measures, which in turn allows assessing the shifting geographies of global science in general and the shifting position of Chinese cities therein in particular. The results show that: (1) on a global scale, the bipolar dominance of Europe and North America has waned in light of the rise of Asia-Pacific and especially China. Most Chinese cities have made significant gains in different centrality measures, albeit that only a handful of cities qualify as world-leading scientific centers. (2) The rise in connectivity of Chinese cities is therefore geographically uneven, as cities along the East Coast and the Yangtze River corridor have become markedly more prominent than cities in other parts of China. The uneven trajectories of Chinese cities can be traced back to changing institutional, economic, and geopolitical contexts. (3) Evolution in the global scientific collaboration network exhibits strong ‘Matthew Effects’, which can be attributed to the path-dependent nature of knowledge production and preferential attachment processes in scientific collaboration.

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.

Scientific collaboration plays an important role in the knowledge production and scientific development. Researchers have investigated numerous aspects of scientific collaboration by constructing scientific collaboration networks. And we can perform node centrality analysis on the scientific collaboration networks to identify important scholars. In these collaboration networks, two scientists are linked if they have coauthored at least one paper and the way of constructing these networks is based on the assumption that each author’s contribution to an article is the same. However, the authors’ contributions to an article are unequal in reality and we should pay attention to the impact of this unequal credit allocation on the understanding of scientific collaboration. In this paper, we regard the first author as the most important contributor to an article and build a directed scientific collaboration network. Then we identify important scholars by analyzing this directed network. For one thing, we investigate the difference between the undirected and directed scientific collaboration network in network properties and centrality analysis. For another, we apply different centrality indices: betweenness, PageRank, SIR and HITS to the directed scientific collaboration network. As a result, we find that each indicator has a different performance and the PageRank algorithm and SIR show highly positive correlation with in-degree. The HITS algorithm also shows better property which can hep us distinguish potential young scholars and identify important collaborators.

Analyzing and visualizing scientific research collaboration network with core node evaluation and community detection based on network embedding

Data-sharing scientific collaborations have particular characteristics, potentially different from the current peer-to-peer environments. In this paper we advocate the benefits of exploiting emergent patterns in self-configuring networks specialized for scientific data-sharing collaborations. We speculate that a peer-to-peer scientific collaboration network will exhibit small-world topology, as do a large number of social networks for which the same pattern has been documented. We propose a solution for locating data in decentralized, scientific, data-sharing environments that exploits the small-worlds topology. The research challenge we raise is: what protocols should be used to allow a self-configuring peer-to-peer network to form small worlds similar to the way in which the humans that use the network do in their social interactions?

Throughout the past three decades, the global pattern of wine production has undergone fundamental changes, most notably the emergence of New World producers. This article presents a detailed account of the sector’s changing global organization from 1974 to 2004 by applying network analysis methods to the evolution of international trade and scientific collaboration networks. We argue that there is a strong mutual interdependence of trade and scientific knowledge production, as a result of which we expect the geographic configuration of global knowledge and trade networks to coevolve. Our results show that, over time, only a few New World wine producers developed trade and scientific collaboration networks that resemble those of traditional Old World producers. They also show that structures of trade and scientific collaboration networks are more alike for Old World than for New World producers, which suggests that, contrary to our expectations, it is particularly Old World producers who may have mainly benefited from participation in international scientific collaboration.