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The purpose of this paper is to analyze the network structure of technology in and between different fields, as well as the evolution of their relations.

Using the patent data in Derwent Innovation Index (DII) from 1991 to 2010, this paper analyzes the co-classification of Derwent Manual Code (DMC) of patents in all technology fields. Large-scaled co-classification matrices are employed to generate the DMC co-classification networks. In addition, analyses are pursued at different levels of aggregation in four five-year windows: 1991-1995, 1996-2000, 2001-2005 and 2006-2010. Using Girvan-Newman algorithm in the clustering process, the structure transformations over time are detected.

The paper identifies the key technological knowledge in certain fields and finds out how different technological fields are connected and integrated. What is more, the dynamic evolution between networks in different time periods reveals the trend of generic technology development in the macroscopic level.

The paper investigates a large quantity of data – all the patent data in DII from 1991 to 2010 in this paper. The paper applies Girvan-Newman algorithm in the co-classification analysis and uses co-classification networks to reveal technology network structures. Evolution coincident with the realistic technological shifts can be observed.

The purpose of this paper is to study a multiple-origin-multiple-destination variant of dynamic critical nodes detection problem (DCNDP) and dynamic critical links detection problem (DCLDP) in stochastic networks. DCNDP and DCLDP consist of identifying the subset of nodes and links, respectively, whose deletion maximizes the stochastic shortest paths between all origins–destinations pairs, in the graph modeling the transport network. The identification of such nodes (or links) helps to better control the road traffic and predict the necessary measures to avoid congestion.

A Markovian decision process is used to model the shortest path problem under dynamic traffic conditions. Effective algorithms to determine the critical nodes (links) while considering the dynamicity of the traffic network are provided. Also, sensitivity analysis toward capacity reduction for critical links is studied. Moreover, the complexity of the underlying algorithms is analyzed and the computational efficiency resulting from the decomposition operation of the network into communities is highlighted.

The numerical results demonstrate that the use of dynamic shortest path (time dependency) as a metric has a significant impact on the identification of critical nodes/links and the experiments conducted on real world networks highlight the importance of sensitive links to dynamically detect critical links and elaborate smart transport plans.

The research in this paper also revealed several challenges, which call for future investigations. First, the authors have restricted our experimentation to a small network where the only focus is on the model behavior, in the absence of historical data. The authors intend to extend this study to very large network using real data. Second, the authors have considered only congestion to assess network’s criticality; future research on this topic may include other factors, mainly vulnerability.

Taking into consideration the dynamic and stochastic nature in problem modeling enables to be effective tools for real-time control of transportation networks. This leads to design optimized smart transport plans particularly in disaster management, to improve the emergency evacuation effeciency.

The paper provides a novel approach to solve critical nodes/links detection problems. In contrast to the majority of research works in the literature, the proposed model considers dynamicity and betweenness while taking into account the stochastic aspect of transport networks. This enables the approach to guide the traffic and analyze transport networks mainly under disaster conditions in which networks become highly dynamic.

Although many interventional approaches have been proposed to address the apparent gap between novices and experts for literature review (LR) search tasks, there have been very few approaches proposed for manuscript preparation (MP) related tasks. The purpose of this paper is to describe a task and an incumbent technique for shortlisting important and unique papers from the reading list (RL) of researchers, meant for citation in a manuscript.

A user evaluation study was conducted on the prototype system which was built for supporting the shortlisting papers (SP) task along with two other LR search tasks. A total of 119 researchers who had experience in authoring research papers participated in this study. An online questionnaire was provided to the participants for evaluating the task. Both quantitative and qualitative analyses were performed on the collected evaluation data.

Graduate research students prefer this task more than research and academic staff. The evaluation measures relevance, usefulness and certainty were identified as predictors for the output quality measure “good list”. The shortlisting feature and information cues were the preferred aspects while limited data set and rote steps in the study were ascertained as critical aspects from the qualitative feedback of the participants.

Findings point out that researchers are clearly interested in this novel task of SP from the final RL prepared during LR. This has implications for digital library, academic databases and reference management software where this task can be included to benefit researchers at the manuscript preparatory stage of the research lifecycle.

Systems to support literature review (LR) and manuscript preparation tend to focus on only one or two of the tasks involved. The purpose of this paper is to describe an intervention framework that redesigns a particular set of tasks, allowing for interconnectivity between the tasks and providing appropriate user interface display features for each task in a prototype system.

A user evaluation study was conducted on the prototype system. The system supports the three tasks: building a reading list (RL) of research papers, finding similar papers based on a set of papers and shortlisting papers from the final RL for inclusion in manuscript based on article type. A total of 119 researchers who had experience in authoring research papers, participated in the evaluation study. They had to select one of the provided 43 topics and execute the tasks offered by the system. Three questionnaires were provided for evaluating the tasks and system. Both quantitative and qualitative analyses were performed on the collected evaluation data.

Task redesign aspects had a positive impact in user evaluation for the second task of finding similar papers while improvement was found to be required for the first and third tasks. The tasks interconnectivity features seed basket and RL were helpful for the participants in conveniently searching for papers within the system. Two of the four proposed informational display features, namely, information cue labels and shared co-relations were the most preferred features of the system. Student user group found the task recommendations and the overall system to be more useful and effective than the staff group.

This study validates the importance of interconnected task design and novel informational display features in accentuating task-based recommendations for LR and manuscript preparatory tasks. The potential for improvement in recommendations was shown through the task redesign exercise where new requirements for the tasks were identified. The resultant prototype system helps in bridging the gap between novices and experts in terms of LR skills.

COVID-19 evolved from a local health crisis to a pandemic and affected countries worldwide accordingly. Similarly, the impacts of the pandemic on the performance of global stock markets could be time-varying. This study applies a dynamic network analysis approaches to evaluate the evolution over time of the impact of COVID-19 on the stock markets' network.

Daily closing prices of 55 global stock markets from August 1, 2019 to September 10, 2020 were retrieved. This sample period was further divided into nine subsample periods for dynamic analysis purpose. Distance matrix based on long-range correlations was calculated, using rolling window's length of 100 trading days, rolled forward at an interval of one month's working days. These distance matrices than used to construct nine minimum spanning trees (MSTs). Network characteristics were figured out, community detection and network rewiring techniques were also used for extracting meaningful from these MSTs.

The findings are, with the evolution of COVID-19, a change in co-movements amongst stock markets' indices occurred. On the 100th day from the date of reporting of the first cluster of cases, the co-movement amongst the stock markets become 100% positively correlated. However, the international investor can still get better portfolio performance with such temporal correlation structure either avoiding risk or pursuing profits. A little change is observed in the importance of authoritative node; however, this central node changed multiple times with change of epicenters. During COVID-19 substantial clustering and less stable network structure is observed.

It is confirmed that this work is original and has been neither published elsewhere, nor it is currently under consideration for publication elsewhere.

The present research is trying to construct the network of relationships between different department of an organization during the design and development of car engine. Analyzing the structure of the network, finding the patterns of collaborations, and determining the important departments are the main purposes of the present research.

Improving relationships during a project life is an effective way to enhance employee performance in project-oriented organizations. This paper examines the collaborative relationships between internal project stakeholders through social network analysis (SNA) in a project for the design and development of car engine. In the first step of the research, the network of internal stakeholders was studied based on collaboration in the common activities performed by the resources. Then, the network of correspondences between internal stakeholders was studied. Finally, the two networks were integrated into a single network.

In the integrated network, the “fuel and combustion department” had the largest degree centrality (i.e. highest collaboration with others). The “integration department” was found to have the highest closeness centrality (i.e. more rapid access to other nodes). Furthermore, the “procurement department” had the highest betweenness centrality (i.e. the most strategic department). Our results revealed the potential capabilities of SNA method for the project management in the vehicle industry.

The obtained results of the present research show us the value of applying SNA methods and concepts to analyze the inter-organizational network of the Project Stakeholders relationship.

This study aims to propose a visualized model of hot technology evolution to describe its development.

The basic concept is to divide a technological field into a timeline consisting of several patent clusters. Hot technology trajectories are then explored using their continuity, as well as the point in time at which they occur.

Patents in orthopaedics between 1999 and 2014 have been chosen as the research subjects and the field is divided into several hot technology trajectories. A further step is taken by interpreting high-frequency key terms. Three categories – spine-related materials, bone repairing materials and bone plates – have been identified.

The trajectories presented by evolving diagrams allow readers to understand the evolution of hot technology and help analysts to plan layout and strategies to remain competitive.

Patent clusters reflect the knowledge context of technology development. Previous studies have focused on only new technology evolution and have rarely explored the knowledge context of hot patents that have been frequently cited in recent years. Such patents often guide the development of technology.

The literature on Maritime Transportation (MT) is experiencing a transition phase where the focus of the research is repositioning. It registered steep growth in recent years with its beginning articles on the concepts of cost minimization to the current focus on achieving sustainable operational effectiveness using Information and Communication Technologies (ICTs). Thus, this becomes a right time to investigate the trajectory of research on MT.

The proposed study aims to explore the potential of data analytics techniques such as data mining and network analytics to reflect the trajectory of research in the maritime supply chain over time. This study identifies the eight main dimensions of the research published under maritime paradigm through network analytics. The in-depth review of these dimensions rendered us to segregate them further into sub-dimensions for the ease of understanding and interpretability. Further, the text mining is employed to extract thematic evolution of the research.

The evolved themes are completely exclusive from the conventional MT research with artificial intelligence, digital storage, waste management and biofuels emerging as contemporary themes. It is found that although there are a sufficient amount of literature on sustainable port practices but their policy implications are still underexplored. The inter-dimension research is needed to achieve the motive of economic efficiency and environmental sustainability simultaneously.

The study has contributed on the methodology side of conducting literature reviews. The dimensions, sub-dimensions and themes are obtained using data analytics tools and techniques. This omits the possibility of personal bias and thus making the results verifiable.

The purpose of this paper is to analyze collaboration networks and their patterns among higher education institutions (HEIs) in Chile and the Latin American region. This will provide evidence to educational managements in order to properly allocate their efforts to improve collaboration.

This quantitative study examines collaboration using a social network analysis (SNA) approach. The main source used to quantify collaboration is co-authorship of scholarly publications retrieved from the Web of Science scientific search engine.

The paper provides evidence that there is a low collaboration rate within-country as well cross-country among HEIs in Latin America. The collaboration network in Chile is highly dependent on two institutions; Pontificia Universidad Católica and Universidad de Chile. These institutions are considered leaders of opinion and knowledge facilitators. The density of the whole network is relatively low; only 5 percent of the potential connections exist in the current network.

The main limitation of this paper is that it does not take into account other possible collaborative efforts such as books, manuscripts, or other types of collaboration that do not result in tangible documents. However, co-authorship based on publications has been considered to be a good estimator of collaboration.

Collaboration is critical to promote research and increase its capacity. The approach presented in this study is helpful for educational managers in charge of allocating resources to effectively have an impact on collaboration. Decision makers will benefit from the evidence-based results generated by the SNA framework.

An understanding of the current status of research collaboration in Latin America allows researchers to detect the main areas of opportunity, which in turn serve to improve future decision making in this area.

Online social networks are increasingly important venues for businesses to promote their products and image. However, information propagation in online social networks is significantly more complicated compared to traditional transmission media such as newspaper, radio, and television. In this chapter, we will discuss research on modeling and forecasting diffusion of virally marketed content in social networks. Important aspects include the content and its presentation, the network topology, and transmission dynamics. Theoretical models, algorithms, and case studies of viral marketing will be explored.