Spatial Structure,Hierarchy and Formation Mechanisms of Scientific Collaboration Networks:Evidence of the Belt and Road Regions

Scientific collaboration has become an important part of the people-to-people exchanges in the Belt and Road initiative,and remarkable progress has been made since 2013.Taking the 65 countries along the Belt and Road(BRI countries)as the research areas and using collaborated Web of Science(WOS)core collection papers to construct an international scientific collaboration matrix,the paper explores the spatial structure,hierarchy and formation mechanisms of scientific collaboration networks of 65 countries along the Belt and Road.The results show that:1)Beyond the Belt and Road regions(BRI regions),Central&Eastern Europe,China and West Asia&North Africa have formed a situation in which they all have the most external links with other countries beyond BRI regions.China has the dominant role over other BRI countries in generating scientific links.The overall spatial structure has changed to a skeleton structure consisting of many dense regions,such as Europe,North America,East Asia and Oceania.2)Within the Belt and Road regions,Central&Eastern Europe has become the largest collaboration partner with other sub-regions in BRI countries.The spatial structure of scientific collaboration networks has transformed from the‘dual core’composed of China and the Central&Eastern Europe region,to the‘multi-polarization’composed of‘one zone and multi-points’.3)The hierarchical structure of scientific collaboration networks presents a typical‘core-periphery’structure,and changes from‘single core’to‘double cores’.4)Among the formation mechanisms of scientific collaboration networks,scientific research strength and social proximity play the most important roles,while geographical distance gradually weakens the hindrance to scientific collaboration.

Clustering approach based on hierarchical expansion for community detection of scientific collaboration network

This study presents a clustering algorithm based on hierarchical expansion to solve the problem of community detection in scientific collaboration network. The characteristics of achievements information related to scientific and technological domains are analyzed,and then an ontology that represents their latent collaborative relations is built to detect clusters from the collaboration network. A case study is conducted to collect a data set of research achievements in the electric vehicle field and better clustering results are obtained. A hierarchical recommendation framework that enriches the domain ontologies and retrieves more relevant information resources is proposed in the last part of this paper. This work also lays out a novel insight into the exploitation of scientific collaboration network to better classify achievements information.

China's landscape in oncology drug research:perspectives from research collaboration networks

Objective： Better understanding of China＇s landscape in oncology drug research is of great significance for discovering anti-cancer drugs in future. This article differs from previous studies by focusing on Chinese oncology drug research communities in co-publication networks at the institutional level. Moreover, this research aims to explore structures and behaviors of relevant research units by thematic community analysis and to address policy recommendations. Methods： This research used social network analysis to define an institutions network and to identify a community network which is characterized by thematic content. Results： A total of 675 sample articles from 2008 through 2012 were retrieved from the Science Citation Index Expanded （SCIE） database of Web of Science, and top institutions and institutional pairs are highlighted for further discussion. Meanwhile, this study revealed that institutions based in the Chinese mainland are located in a relatively central position, Taiwan＇s institutions are closely assembled on the side, and Hong Kong＇s units located in the middle of the Chinese mainland＇s and Taiwan＇s. Spatial division and institutional hierarchy are still critical barriers to research collaboration in the field of anti-cancer drugs in China. In addition, the communities focusing on hot research areas show the higher nodal degree, whereas communities giving more attention to rare research subjects are relatively marginalized to the periphery of network. Conclusions= This paper offers policy recommendations to accelerate cross-regional cooperation, such as through developing information technology and increasing investment. The brokers should focus more on outreach to other institutions. Finally, participation in topics of common interest is conducive to improved efficiency in research and development （R＆D） resource allocation.

A Collaboration Network Model with Multiple Evolving Factors

To describe the empirical data of collaboration networks, several evolving mechanisms have been proposed, which usually introduce different dynamics factors controlling the network growth. These models can reasonably reproduce the empirical degree distributions for a number of we11-studied real-world collaboration networks. On the basis of the previous studies, in this work we propose a collaboration network model in which the network growth is simultaneously controlled by three factors, including partial preferential attachment, partial random attachment and network growth speed. By using a rate equation method, we obtain an analytical formula for the act degree distribution. We discuss the dependence of the act degree distribution on these different dynamics factors. By fitting to the empirical data of two typical collaboration networks, we can extract the respective contributions of these dynamics factors to the evolution of each networks.

Three vertex degree correlations of fixed act-size collaboration networks

A rate equation approach was presented for the exact computation of the three vertex degree correlations of the fixed act-size collaboration networks.Measurements of the three vertex degree correlations were based on a rate equation in the continuous degree and time approximation for the average degree of the nearest neighbors of vertices of degree k,with an appropriate boundary condition.The rate equation proposed can be generalized in more sophisticated growing network models,and also extended to deal with related correlation measurements.Finally,in order to check the theoretical prediction,a numerical example was solved to demonstrate the performance of the degree correlation function.

Manufacturing enterprise collaboration network:An empirical research and evolutionary model

With the increasingly fierce market competition,manufacturing enterprises have to continuously improve their competitiveness through their collaboration and labor division with each other,i.e.forming manufacturing enterprise collaborative network(MECN)through their collaboration and labor division is an effective guarantee for obtaining competitive advantages.To explore the topology and evolutionary process of MECN,in this paper we investigate an empirical MECN from the viewpoint of complex network theory,and construct an evolutionary model to reproduce the topological properties found in the empirical network.Firstly,large-size empirical data related to the automotive industry are collected to construct an MECN.Topological analysis indicates that the MECN is not a scale-free network,but a small-world network with disassortativity.Small-world property indicates that the enterprises can respond quickly to the market,but disassortativity shows the risk spreading is fast and the coordinated operation is difficult.Then,an evolutionary model based on fitness preferential attachment and entropy-TOPSIS is proposed to capture the features of MECN.Besides,the evolutionary model is compared with a degree-based model in which only node degree is taken into consideration.The simulation results show the proposed evolutionary model can reproduce a number of critical topological properties of empirical MECN,while the degree-based model does not,which validates the effectiveness of the proposed evolutionary model.

Measuring author influence in scientific collaboration networks

Purpose:The purpose of this paper is to measure author influence in scientific collaboration networks by considering the combined effects of multiple indexes.In the meanwhile,we intend to explore a method to avoid assigning subjective weights.Design/methodology/approach:We applied four centrality measures(degree centrality,betweenness centrality,closeness centrality and eigenvector centrality)and authors’published papers to the scientific collaboration network.The grey relational analysis(GRA)method based on information entropy was used to measure an author’s impact in the collaboration network.The weight of each evaluation index was determined based on information entropy.The ACM SIGKDD collaboration network was selected as an example to demonstrate the practicality and effectiveness of our method.Findings:Author influence was not always positively correlated with evaluation indexes such as degree centrality and betweenness centrality.This implies that combined effects of multiple indexes should be considered in author impact analysis.The introduction of the GRA method based on information entropy can reduce the interference of human factors in the evaluation process.Research limitations:We only analyzed author influence from the perspective of scientific collaboration,but the impact of citation on author influence was ignored.Practical implications:The proposed method can be also applied to detect influential authors in bibliographic co-citation network,author co-citation network,bibliographic coupling network or author coupling network.It would help facilitate scientific collaboration and enhance scholarly communication.Originality/value:This paper proposes an analytical method of evaluating author influence in scientific collaboration networks,in which combined effects of multiple indexes are considered and the interference of human factors is reduced in the evaluation process.

Evolution model for scientific collaboration network with local-world information

In order to reveal the intrinsic properties of scientific collaboration networks, a new local-world evolution model on a scientific collaboration network is proposed by analysing the network growth mechanism. The act degree as the measurement of preferential attachment is taken, and the local-world information of nodes is taken into account. Analysis and simulation show that the node degree and the node strength obey the power-law distribution. Low average path length and high clustering coefficient are approved. Experiment indicates that the model can depict efficiently the topological structure and statistical characteristics of real-life scientific collaboration networks.

Diffusion of municipal wastewater treatment technologies in China： a collaboration network perspective

The diffusion of municipal wastewater treatment technology is vital for urban environment in developing countries. China has built more than 3000 municipal wastewater treatment plants in the past three decades, which is a good chance to understand how technologies diffused in reality. We used a data-driven approach to explore the relationship between the diffusion of wastewater treatment technologies and collaborations between organizations. A database of 3136 municipal wastewater treatment plants and 4634 collaborating organizations was built and transformed into networks for analysis. We have found that： 1）.the diffusion. networks are assortative, arid the .pattems of diffusion vary across technologies; while the collaboration networks are fragmented, and have an assortativity around zero since the 2000s. 2） Important projects in technology diffusion usually involve central organizations in collaboration networks, but organizations become more central in collaboration by doing circumstantial projects in diffusion. 3）The importance of projects in diffusion can be predicted with a Random Forest-model at a good accuracy and precision level. Our findings provide a quantitative understanding of the technology diffusion processes, which could be used for waterrelevant policy-making and business decisions.

Collaboration networks evolution of Safety Science from 1976 to 2019

There is a recent interest to understand the nature of the safety science discipline and to obtain insights in its development patterns and research trends.This article analyzes the evolution of the prevalence and scale of collaborative publishing and the macro-level collaboration scale of the Safety Science research community.Additionally,an analysis of the evolution of influential research topics of the core researchers’collaboration networks provides insights in the domain’s high-level development trends.Both the prevalence and scale of scientific collaboration are found to have increased dramatically since the inception of Journal of Occupational Accidents,Safety Science’s predecessor.Research networks have grown significantly,and collaboration between core researchers has steadily increased.Even though this indicates that a core safety science research community has developed,it is also found that the journal continues to serve as a platform for many small and unconnected author clusters.In terms of influential research topics,there is a notable shift from technical aspects of work safety towards psychological and organizational mechanisms of safety.More recently,influential work of core research networks has additionally focused on safety and risk models and methods,the conceptual and theoretical foundations of the domain,and influential research clusters have formed around safety in specific industries.The focus topics of core researcher’s collaboration clusters furthermore highlight the variety of conceptual,theoretical,and methodological approaches co-existing within Safety Science.Various implications of the findings are discussed,where both possible benefits and drawbacks of increased collaboration are highlighted and future research avenues outlined.

Step-by-Step Random Walk Network with Power-Law Clique-Degree Distribution

We propose a simple mechanism for generating scale-free networks with degree exponent γ=3, where the new node is connected to the existing nodes by step-by-step random walk. It is found that the clique-degree distribution based on our model obeys a power-law form, which is in agreement with the recently empirical evidences. In addition, our model displays the small-world effect and the hierarchical structure.

A comparative study on institutional collaboration characteristics in EM and LIS in China

Purpose： This study analyzes the current status of institutional cooperation in economics and management （EM） and library and information science （LIS） in China.Design/methodology/approach： Based on the Chinese Social Science Citation Index （CSSCI） database, we constructed institutional collaboration networks in EM and LIS, and analyzed the collaboration characteristics through social network analysis.Findings： In the development and evolution of the collaboration network of institutions in humanities and social sciences, EM is always at the center. It has extensive cooperation relationships with other fields. The position of LIS has also become centralized, and its interdisciplinary cooperation has increased. For both EM and LIS, we observed ＂small-world＂ and ＂scale-free＂ networks, indicating full communication and mature development in both disciplines. Based on a comparison of two institutions in the two fields, we confirmed the comprehensive development in EM and the extensive information exchange in LIS.Research limitations： We collected data only from humanities and social sciences, but did not consider the connection between EM and natural sciences, or between LIS and natural sciences. In addition, the paper lacks analysis of institutional collaboration at the micro level.Practical implications： The paper provides insights into the institutional cooperation characteristics in EM and LIS in China.Originality/value： The paper offers a new perspective on the characteristics of institutional collaboration in China.

Deep Reinforcement Learning-Based URLLC-Aware Task Offloading in Collaborative Vehicular Networks

Collaborative vehicular networks is a key enabler to meet the stringent ultra-reliable and lowlatency communications(URLLC)requirements.A user vehicle(UV)dynamically optimizes task offloading by exploiting its collaborations with edge servers and vehicular fog servers(VFSs).However,the optimization of task offloading in highly dynamic collaborative vehicular networks faces several challenges such as URLLC guaranteeing,incomplete information,and dimensionality curse.In this paper,we first characterize URLLC in terms of queuing delay bound violation and high-order statistics of excess backlogs.Then,a Deep Reinforcement lEarning-based URLLCAware task offloading algorithM named DREAM is proposed to maximize the throughput of the UVs while satisfying the URLLC constraints in a besteffort way.Compared with existing task offloading algorithms,DREAM achieves superior performance in throughput,queuing delay,and URLLC.

XML-based Data Processing in Network Supported Collaborative Design

In the course of network supported collaborative design, the data processing plays a very vital role. Much effort has been spent in this area, and many kinds of approaches have been proposed. Based on the correlative materials, this paper presents extensible markup language （XML） based strategy for several important problems of data processing in network supported collaborative design, such as the representation of standard for the exchange of product model data （STEP） with XML in the product information expression and the management of XML documents using relational database. The paper gives a detailed exposition on how to clarify the mapping between XML structure and the relationship database structure and how XML-QL queries can be translated into structured query language （SQL） queries. Finally, the structure of data processing system based on XML is presented.

Stability and Generalization of Hypergraph Collaborative Networks

Graph neural networks have been shown to be very effective in utilizing pairwise relationships across samples.Recently,there have been several successful proposals to generalize graph neural networks to hypergraph neural networks to exploit more com-plex relationships.In particular,the hypergraph collaborative networks yield superior results compared to other hypergraph neural net-works for various semi-supervised learning tasks.The collaborative network can provide high quality vertex embeddings and hyperedge embeddings together by formulating them as a joint optimization problem and by using their consistency in reconstructing the given hy-pergraph.In this paper,we aim to establish the algorithmic stability of the core layer of the collaborative network and provide generaliz--ation guarantees.The analysis sheds light on the design of hypergraph filters in collaborative networks,for instance,how the data and hypergraph filters should be scaled to achieve uniform stability of the learning process.Some experimental results on real-world datasets are presented to illustrate the theory.

Entrepreneurial Orientation and Innovation Ecosystems in the Industrial Sector, Central Region, Kampala, Uganda: A Review

This review explores entrepreneurial orientation and innovation ecosystems in the industrial sector of the Central Region, Kampala, Uganda, through an analysis of ten scholarly articles. The study contextualizes the research within the regional landscape and establishes a theoretical framework through a focused literature review. Key findings highlight the intersection of entrepreneurial activities and innovation dynamics, emphasizing the region’s unique contributions to the broader field. Discussions on discrepancies and unexplored territories within the articles offer insights into limitations and research gaps. The manuscript concludes by identifying future research avenues, providing a roadmap for ongoing inquiry into the entrepreneurial and innovative dimensions of the Central Region’s industrial sector. This synthesis underscores the importance of cultivating an entrepreneurial mindset and collaborative innovation strategies for sustainable industrial development in the region.

A Study of Methods to Identify Industry-University-Research Institution Cooperation Partners based on Innovation Chain Theory

Purpose： This study aims at identifying potential industry-university-research collaboration（IURC） partners effectively and analyzes the conditions and dynamics in the IURC process based on innovation chain theory.Design/methodology/approach： The method utilizes multisource data, combining bibliometric and econometrics analyses to capture the core network of the existing collaboration networks and institution competitiveness in the innovation chain. Furthermore, a new identification method is constructed that takes into account the law of scientific research cooperation and economic factors.Findings： Empirical analysis of the genetic engineering vaccine field shows that through the distribution characteristics of creative technologies from different institutions, the analysis based on the innovation chain can identify the more complementary capacities among organizations.Research limitations： In this study, the overall approach is shaped by the theoretical concept of an innovation chain, a linear innovation model with specific types or stages of innovation activities in each phase of the chain, and may, thus, overlook important feedback mechanisms in the innovation process.Practical implications： Industry-university-research institution collaborations are extremely important in promoting the dissemination of innovative knowledge, enhancing the quality of innovation products, and facilitating the transformation of scientific achievements.Originality/value： Compared to previous studies, this study emulates the real conditions of IURC. Thus, the rule of technological innovation can be better revealed, the potential partners of IURC can be identified more readily, and the conclusion has more value.

Cloud Computing-Based Forensic Analysis for Collaborative Network Security Management System

Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets, well-organized distributed network attacks, consist of a large number of bots that generate huge volumes of spam or launch Distributed Denial of Service （DDoS） attacks on victim hosts. New emerging botnet attacks degrade the status of Internet security further. To address these problems, a practical collaborative network security management system is proposed with an effective collaborative Unified Threat Management （UTM） and traffic probers. A distributed security overlay network with a centralized security center leverages a peer-to-peer communication protocol used in the UTMs collaborative module and connects them virtually to exchange network events and security rules. Security functions for the UTM are retrofitted to share security rules. In this paper, we propose a design and implementation of a cloud-based security center for network security forensic analysis. We propose using cloud storage to keep collected traffic data and then processing it with cloud computing platforms to find the malicious attacks. As a practical example, phishing attack forensic analysis is presented and the required computing and storage resources are evaluated based on real trace data. The cloud- based security center can instruct each collaborative UTM and prober to collect events and raw traffic, send them back for deep analysis, and generate new security rules. These new security rules are enforced by collaborative UTM and the feedback events of such rules are returned to the security center. By this type of close-loop control, the collaborative network security management system can identify and address new distributed attacks more quickly and effectively.

Collaborative Network Security in Multi-Tenant Data Center for Cloud Computing

A data center is an infrastructure that supports Internet service. Cloud comput the face of the Internet service infrastructure, enabling even small organizations to quickly ng is rapidly changing build Web and mobile applications for millions of users by taking advantage of the scale and flexibility of shared physical infrastructures provided by cloud computing. In this scenario, multiple tenants save their data and applications in shared data centers, blurring the network boundaries between each tenant in the cloud. In addition, different tenants have different security requirements, while different security policies are necessary for different tenants. Network virtualization is used to meet a diverse set of tenant-specific requirements with the underlying physical network enabling multi-tenant datacenters to automatically address a large and diverse set of tenants requirements. In this paper, we propose the system implementation of vCNSMS, a collaborative network security prototype system used n a multi-tenant data center. We demonstrate vCNSMS with a centralized collaborative scheme and deep packet nspection with an open source UTM system. A security level based protection policy is proposed for simplifying the security rule management for vCNSMS. Different security levels have different packet inspection schemes and are enforced with different security plugins. A smart packet verdict scheme is also integrated into vCNSMS for ntelligence flow processing to protect from possible network attacks inside a data center network