Structure Of Knowledge Networks Research Articles

Architectural styles of curiosity in global Wikipedia mobile app readership.

Intrinsically motivated information seeking is an expression of curiosity believed to be central to human nature. However, most curiosity research relies on small, Western convenience samples. Here, we analyze a naturalistic population of 482,760 readers using Wikipedia's mobile app in 14 languages from 50 countries or territories. By measuring the structure of knowledge networks constructed by readers weaving a thread through articles in Wikipedia, we replicate two styles of curiosity previously identified in laboratory studies: the nomadic "busybody" and the targeted "hunter." Further, we find evidence for another style-the "dancer"-which was previously predicted by a historico-philosophical examination of texts over two millennia and is characterized by creative modes of knowledge production. We identify associations, globally, between the structure of knowledge networks and population-level indicators of spatial navigation, education, mood, well-being, and inequality. These results advance our understanding of Wikipedia's global readership and demonstrate how cultural and geographical properties of the digital environment relate to different styles of curiosity.

Revisiting the Paradoxes of Knowledge Diversity and Network Structure for Team Innovation

Revisiting the Paradoxes of Knowledge Diversity and Network Structure for Team Innovation

A STUDY ON THE INFLUENCE OF KNOWLEDGE NETWORK STRUCTURE EMBEDDING ON ENTERPRISE INNOVATION PERFORMANCE: BASED ON THE MEDIATION EFFECT OF TECHNOLOGICAL DIVERSIFICATION

With the continuous changes in the environment and the gradual enhancement of market competitiveness, knowledge and information have become an important foundation for enterprise development, and knowledge elements have become important factors affecting enterprise innovation. The trend of knowledge networking is becoming increasingly evident. The knowledge network formed from this can break the traditional pattern of internal circular innovation, provide enterprises with many heterogeneous resources, and improve innovation performance. The cross integration of different technologies makes the knowledge network denser and more complex. How to continuously acquire and develop heterogeneous resources through knowledge networks? How to promote innovation performance of enterprises through rational allocation and utilization of technological diversification? The in-depth study of this series of issues has important theoretical and practical significance. This study will conduct a series of theoretical and empirical studies on the impact of knowledge network structure embedding on enterprise innovation performance. It will provide ideas and references for promoting sustainable innovation in enterprises.

Knowledge network structure and middle management involvement as determinants of TMT members’ ambidexterity: A multilevel analysis

Even though scholars have widely asserted that ambidextrous organizations need ambidextrous managers, we still know relatively little about the emergence of ambidextrous behaviours among top management team (TMT) members. The purpose of this study is to gain a deeper understanding about how TMT networks and middle management involvement in decision-making collectively shape the ability of TMT members to deal successfully with the exploration–exploitation paradox. Through in-depth interviews and a multilevel analysis of internal networks of 123 TMT members in 20 public hospitals, results suggest that leaders' centrality and the density of TMT networks shape the ability of TMT members to behave ambidextrously. The framework of this paper also incorporates the role of middle management, critical in linking the strategic apex and the operating core, and thereby supporting TMT members’ ambidextrous behaviour. Various theoretical and practical implications are discussed.

An Analysis of the Categories and Structures of Expertise for Students’ Cognitive States

Knowledge networks play an important role in the process of knowledge acquisition and sharing by students. An analysis of their complex structural features is required for the connectivity between students and knowledge. Existing research lacks insight into the internal structural features of knowledge networks constructed from expertise. There is also a lack of effective methods for constructing personalised knowledge networks for students' cognitive states. This paper analyses the categories and structures of expertise for students' cognitive states, and presents in detail a grey prediction algorithm to identify students' cognitive states. Then, the paper presents a typological description of the knowledge nodes in the expertise network for students' cognitive states, and analyses the knowledge network structure from the perspectives of paths and statistical properties. After that, the paper gives a method for analysing the knowledge flow of the expertise network. The experimental results validate the effectiveness of the proposed method.

The population ecology of sustainable agriculture knowledge networks: insights from California

Sustainable agricultural knowledge networks consist of heterogeneous actors who collaborate and share knowledge to advance the goals of sustainable agriculture. We analyze how the structure of sustainable agriculture knowledge networks is related to the social-ecological context across 57 counties in California. We apply a population ecology approach that identifies variables related to the space, energy, and stability of the social-ecological context in which networks evolve. Using data from a 2016 survey of agricultural outreach and extension professionals, we find four different types of networks at different stages of development, which vary in the centrality of key actors associated with the University of California. The most highly developed networks exist in agriculturally productive counties with diverse crops. The population ecology variables relate differently to the size versus structure of knowledge networks, which implies a need for ambidextrous extension strategies customized to different types of network processes.

Influence Mechanism of Knowledge Network on Regional Innovation Capability Based on Hydrogen Energy Industry

As an important factor of innovation, knowledge plays a vital role in promoting the growth of innovation ability. In recent years, many scholars have explored the influence of knowledge network structure on innovation. The hydrogen energy industry is highly technical and covers a wide range, which is in urgent need of regional cooperation and easy to form a knowledge network. The function of the knowledge network depends on inter-organizational communication, so traffic development may be a boundary condition. This paper takes the hydrogen energy industry as an example to study the influence mechanism of the knowledge network on regional innovation ability and explore the moderate effect of high-speed rail opening. This paper takes hydrogen patents as samples, uses backward citation data to construct the knowledge network, and conducts regression tests. The results show that the density of the knowledge network has a negative effect on regional innovation ability, while the structural hole and centrality have a positive effect. The opening of high-speed rail will strengthen the influence of density and structural holes, and weaken the influence of centrality. Finally, it is proposed that the development of the hydrogen energy industry should reduce the dependence on the network, and the government should prevent the loss of large enterprises.

Linking networking capability and ambidextrous innovation strategies: the mediating roles of knowledge assimilation application and knowledge transformation application

PurposeFrom the perspective of social networks and knowledge networks, this study aims to empirically examine an updated four-dimension networking capability (NC) construct and test the relationships between it and innovation strategies through knowledge application activities in the context of Chinese manufacturing firms.Design/methodology/approachData analysis is conducted on a quantitative survey of 219 manufacturing firms in China with a mixed method of partial least squares structural equation modeling and fuzzy-set qualitative comparative analysis.FindingsThe results indicate that knowledge assimilation application (KAA) and knowledge transformation application (KTA) fully mediate the relationships between NC and ambidextrous innovation strategies. KAA has a stronger association with exploitative innovation (EL) than with exploratory innovation (ER), and KTA has a stronger association with ER than with EL.Research limitations/implicationsThe authors develop the conceptualization of NC and examine its role in knowledge application activities within a firm. Further, the authors adopt an alternative perspective to highlight the importance of KAA and KTA in mediating the relationships between NC and exploratory and exploitative innovation strategies.Practical implicationsThe findings indicate that firms should build NC based on their knowledge requirements and knowledge network structure. Additionally, managers should possess in-depth insights regarding an effective knowledge application toward different types of external knowledge from partners. More specific, firms are more likely to conduct KAA for a high degree of knowledge relatedness, while for a low degree of knowledge relatedness, firms are more likely to conduct KTA.Originality/valueThe authors provide a novel alternative insight into knowledge application activities. From the perspective of knowledge networks, the authors argue that there are two distinct and parallel activities (i.e. KAA and KTA). The authors empirically examined the mediating roles of KAA and KTA in the NC–ambidextrous innovation strategies relationships as well as enriched the literature on their relationships.

Remote Access Memories: How Remote Work Will Impact Organizational Learning and What Firms Can Do

Is the increase in the popularity of remote work something organizations should resist or embrace? Which organizations stand to gain from this new trend, and how can they prepare for or mitigate negative consequences of remote work? Using a computational model, we show that complexity and turbulence in the organizational environment as well as the structure of organizational knowledge networks play a crucial role in determining the benefits of employees working remotely. Our results suggest that remote work is beneficial for organizations operating in simpler and less turbulent environments. These organizations also benefit from more disconnected knowledge networks, while those operating in complex and turbulent environments should consider switching to flatter and more interconnected networks when implementing remote work. We shed light on the likely effects of the rise in remote work among organizations and highlight the role organizational design can play in preparing organizations for this new reality.

STRUCTURAL EVOLUTION OF KNOWLEDGE NETWORK AND BEHAVIOR VARIATION OF KNOWLEDGE SUBJECT DRIVEN BY EMOTIONAL BEHAVIOR

BackgroundThe perception behavior of the knowledge subjects can get certain feedback and enlightenment from the trend variation of knowledge level. The change of the psychological expectation and behavior of the knowledge subjects is of great significance to the evolution and utilization of the knowledge network structure in the complex system. At present, there have been many mature researches on the content of knowledge flow and knowledge diffusion between organizations based on network structure. This article combines the relevant theories of micro-behavioral psychology from the perspective of complex system evolution, to further explore the influences of four typical complex network structures evolution on the knowledge transfer behavior of the knowledge subjects and the knowledge level of the subjects.Subjects and MethodsIn this paper, a simulation research process is designed based on a complex knowledge network model. The calculation indicators mainly include three aspects: network value, node coupling costs, and weight values of relational edges. The node coupling costs are composed of three dimensions: the psychological distance cost between network nodes, the difference in cooperation value between network nodes, and the difference in connectivity between network nodes. Using programming, four programs are used to simulate and analyze the evolution of complex network structure based on the coupling cost of the knowledge subjects and the psychological expectations and behavior changes of the knowledge subjects.ResultsThe results of this paper show that the more frequent the interaction between network subjects, which is not necessarily greater beneficial to the knowledge subjects. In addition, the fair distribution of knowledge resources is not conducive to the growth of knowledge level. Among the four types of typical network evolution, the development of the small-world network structure is relatively stable, and the adaptive behavior of the knowledge subjects has produced relatively high expected value.ConclusionsThe stability of the network environment has an exogenous impact on the expectation and value level of the knowledge subjects. The development environment of the evolution of the small-world network structure is relatively stable, which can generate high psychological expectations for the adaptive behavior of the knowledge subjects. It provides a reference value for the knowledge transfer of the knowledge subjects in the evolution of the knowledge network. This article discusses the behavioral changes of network subjects from the perspective of the psychology of knowledge subjects, which has certain practical significance. However, it does not consider the initial endowment differences between the nodes and the alternate evolution process between complex networks. Therefore, follow-up researches can further optimize the understanding on the differences between subjects in the complex network and analyze the adaptive behavior of knowledge subjects.AcknowledgementsSupported by projects grant from National Natural Science Foundation of China (Grant No. 71602012); Humanities and Social Sciences Fund of the Ministry of Education (Grant No. 21YJC790122); and Philosophy and Social Science Fund Project of CDUT (Grant No. YJ2020-JX002).

Investigating the network structure of domain-specific knowledge using the semantic fluency task.

Cognitive scientists have a long-standing interest in quantifying the structure of semantic memory. Here, we investigate whether a commonly used paradigm to study the structure of semantic memory, the semantic fluency task, as well as computational methods from network science could be leveraged to explore the underlying knowledge structures of academic disciplines such as psychology or biology. To compare the knowledge representations of individuals with relatively different levels of expertise in academic subjects, undergraduate students (i.e., experts) and preuniversity high school students (i.e., novices) completed a semantic fluency task with cue words corresponding to general semantic categories (i.e., animals, fruits) and specific academic domains (e.g., psychology, biology). Network analyses of their fluency networks found that both domain-general and domain-specific semantic networks of undergraduates were more efficiently connected and less modular than the semantic networks of high school students. Our results provide an initial proof-of-concept that the semantic fluency task could be used by educators and cognitive scientists to study the representation of more specific domains of knowledge, potentially providing new ways of quantifying the nature of expert cognitive representations.

Robustness of knowledge networks under targeted attacks: Electric vehicle field of China evidence

A new framework is proposed to comprehensively explore the robustness (structure and function) of knowledge networks under different targeted attacks. Results show that (1) recalculated-based attacks cause greater damage than initial-based attacks. Meanwhile, a higher price is needed to destroy the structure than disrupt the function of the knowledge network. (2) Maintaining the structure of the knowledge network not only depends on the position that high-degree knowledge elements occupy in the maximal connected subgraph and their upper limit of combinatorial value but also relies on the reducing requirement of new knowledge elements caused by local search and connections. (3) Embedding more knowledge elements that are involved in multiple knowledge domains with medium-degree distribution can consolidate the knowledge network's structure. With regard to enhancing network efficiency, highly relevant knowledge elements create an inverted U-shape influence, while diverse knowledge elements continuously exert positive effects.

Mapping Network Structure and Diversity of Interdisciplinary Knowledge in Recommended MOOC Offerings

In massive open online courses (MOOCs), recommendation relationships present a collection of associations that imply a new form of integration, such as an interdisciplinary synergy among diverse disciplines. This study took a computer science approach, using the susceptible-infected (SI) model to simulate the process of learners accessing courses within networks of MOOC offerings, and emphasized the potential effects of a network structure. The current low rate of access suggests that a ceiling effect influences learners’ access to learning online, given that there are thousands of courses freely available. Interdisciplinary networks were created by adding recommended courses into four disciplinary networks. The diversity of interdisciplinarity was measured by three attributes, namely variety, balance, and disparity. The results attest to interesting changes in how the diversity of interdisciplinary knowledge grows. Particularly remarkable is the degree to which the diversity of interdisciplinarity increased when new recommended courses were first added. However, changing diversity implied that neighbouring disciplines were more likely to come to the forefront to attach to the interdisciplinarity of MOOC offerings, and that the pace of synergy among disparate disciplines slowed as time passed. In the absence of domain experts, expert knowledge is not sufficient to support interdisciplinary curriculum design. More evidence-based analytics studies showing how interdisciplinarity evolves in course offerings could help us to better design online courses that prepare learners with 21st-century skills.

Technology Network Structure Conditions the Economic Resilience of Regions

abstract This article assesses the network robustness of the technological capability base of 269 European metropolitan areas against the potential elimination of some of their capabilities. By doing so, it provides systematic evidence on how network robustness conditioned the economic resilience of these regions in the context of the 2008 economic crisis. The analysis concerns calls in the relevant literature for more in-depth analysis on the link between regional economic network structures and the resilience of regions to economic shocks. By adopting a network science approach that is novel to economic geographic inquiry, the objective is to stress test the technological resilience of regions by utilizing information on the coclassification of CPC (Cooperative Patent Classification) classes listed on European Patent Office patent documents. We find that European metropolitan areas show heterogeneous levels of technology network robustness. Further findings from regression analysis indicate that metropolitan regions with a more robust technological knowledge network structure exhibit higher levels of resilience with respect to changes in employment rates. This finding is robust to various random and targeted elimination strategies concerning the most frequently combined technological capabilities. Regions with high levels of employment in industry but with a vulnerable technological capacity base are particularly challenged by this aspect of regional economic resilience.

Investigating Cognitive Network Models of Learners’ Knowledge Representations

This commentary discusses how research approaches from Cognitive Network Science can be of relevance to research in the field of Learning Analytics, with a focus on modelling the knowledge representations of learners and students as a network of interrelated concepts. After providing a brief overview of research in Cognitive Network Science, I suggest that a focus on the cognitive processes that occur in the knowledge network, as well as the mechanisms that give rise to changes in the structure of knowledge networks, can lead to potentially informative insights into how learners navigate their knowledge representations to retrieve information and how the knowledge representations of learners develop and grow over the course of their educational careers. Learning Analytics can leverage these insights to design adaptive learning or online learning platforms that optimize learning, and inform pedagogical practice and assessment design that support the development of effective and robust knowledge structures.

Policy influence in the knowledge space: a regional application

Cluster policies aim at improving collaboration between co-located actors to address systemic failures. As yet, cluster policy evaluations are mainly concerned with effects on firm performance. Some recent studies move to the system level by assessing how the structure of actor-based knowledge networks is affected by such policies. We continue in that direction and analyze how technology-based regional knowledge spaces are shaped by the introduction of a cluster policy. Taking the example of the German BioRegio contest, we examine how such knowledge spaces in winning and non-winning regions evolved before, during and after the policy. Using a difference-in-differences approach, we identify treatment effects of increased knowledge space embeddedness of biotechnology only in the post-treatment period. Our findings imply that cluster policies can have long-term structural effects typically not accounted for in policy evaluations.

Use of Triangulation in Comparing the Blockchain Knowledge Structure between China and South Korea: Scientometric Network, Topic Modeling, and Prediction Technique

Blockchain, as a new innovative technology, has become a popular topic in many fields in recent years. In this study, triangulation was used to investigate the development of knowledge structures. First, scientometric network analysis was employed to identify the cooperation of knowledge networks. It was found that the structure of blockchain knowledge networks in China is relatively more complex and diverse than in South Korea. Since increased teamwork in blockchain is conducive to the creation of high-quality knowledge products, the Chinese government appears to strongly promote diversified cooperation on blockchain technology through centralized policies. Second, machine-learning topic modeling was used to analyze the content exchanged via a collaborative network. As a result, it was found that both countries lacked the societal and commercial aspects of blockchain technology. Finally, we developed a prediction technique based on the Ernie model to automatically categorize the nature of blockchain research.

Bibliometric trends in priapism research publications

To explore the cooperation of authors, institutions, countries and find hot topics of priapism. Papers about priapism were retrieved from the Web of Science Core Collection (WoSCC). VOSviewer and CiteSpace were used to conduct the bibliometric analyses. Network maps were generated to evaluate the collaborations between different keywords, institutions, authors and countries. A total of 2485 articles related to study of priapism were identified. We observed gradually increasing in the number of publications from 1970 to 2012, and the trend was to be relatively stable in the past ten years. Journal of Urology (322 papers) owned the highest number of publications and Journal of Urology was the most co-cited journal. Burnett, AL (106 papers) was the top most productive authors and the largest numbers of citations (1703 co-citations) during the past decades. There were active collaborations among the top authors. The USA was the leading contributor in this field with 820 papers. Cooperation between countries and between institutions was observed. The main hot topics included matters related to priapism, management, erectile dysfunction, penile erection, penis, diagnosis and high-flow priapism. This paper uses visualization tools to draw a knowledge map of priapism research, and provides a new perspective and direction for priapism research by clarifying the time series, development context, research hotspots, knowledge network structure and the status of major scholars in priapism.

Optimal degree of openness in open innovation: A perspective from knowledge acquisition & knowledge leakage

Open innovation has become an important mode for firms to improve innovation performance. If the openness is too low, it will reduce the opportunity to acquire external knowledge, while too open will increase the risk of internal knowledge leakage. Therefore, the degree of openness plays a key role in the use of open innovation to improve innovation performance. We use the openness breadth and openness depth to describe the openness of innovation. We conduct an agent-based simulation methodology to study the optimal degree of openness. Our findings show that there are great differences in the optimal degree of openness under different situations. Moreover, as the situation changes, the optimal degree of openness changes regularly. More specifically, the more a firm's knowledge network structure holes, the smaller the optimal openness breadth and the greater the optimal openness depth. The higher a firm's knowledge network centrality, the greater the optimal openness breadth and the smaller the optimal openness depth. The worse the institutional environment, the smaller the optimal openness breadth and openness depth. The fiercer the market competition, the greater the optimal openness breadth and the smaller the optimal openness depth.

Structural Dynamics of Knowledge Networks

We investigate the structural patterns of the appearance and disappearance of links in dynamic knowledge networks. Human knowledge is nowadays increasingly created and curated online, in a collaborative and highly dynamic fashion. The knowledge thus created is interlinked in nature, and an important open task is to understand its temporal evolution. In this paper, we study the underlying mechanisms of changes in knowledge networks which are of structural nature, i.e., which are a direct result of a knowledge network's structure. Concretely, we ask whether the appearance and disappearance of interconnections between concepts (items of a knowledge base) can be predicted using information about the network formed by these interconnections. In contrast to related work on this problem, we take into account the disappearance of links in our study, to account for the fact that the evolution of collaborative knowledge bases includes a high proportion of removals and reverts. We perform an empirical study on the best-known and largest collaborative knowledge base, Wikipedia, and show that traditional indicators of structural change used in the link analysis literature can be classified into four classes, which we show to indicate growth, decay, stability and instability of links. We finally use these methods to identify the underlying reasons for individual additions and removals of knowledge links.