Using Network Science to Support Design Research: From Counting to Connecting

Technological innovations at the forefront of industrial revolutions are applied not only in industry but also create a need for research and applications, as a result of which they are mastered and penetrate other areas of human activity. In this regard, the latest advances in information technology in the field of data generation, storage, transmission and processing during the 4th industrial revolution have led to the transformation of traditional scientific activity and the rapid development of the concept of “data-based science”. The article analyzes the main development stages in the scientific environment, the organization of scientific activities with the widespread use of Industry 4.0 solutions, and its organic connection with the concept of Science 4.0. The essence of Science 4.0 is revealed through the review of studies in the field of the Internet of Things, cyber-physical systems, artificial intelligence, cloud computing, big data analytics, and other intelligent solutions. Conceptual issues of the formation of Science 4.0 are developed and relevant proposals are made for its implementation.

We strive to understand care coordination structures of multidisciplinary teams and to evaluate their effect on post-surgical length of stay (PSLOS) in the Neonatal Intensive Care Unit (NICU). Electronic health record (EHR) data were extracted for 18 neonates, who underwent gastrostomy tube placement surgery at the Vanderbilt University Medical Center NICU. Based on providers' interactions with the EHR (e.g. viewing, documenting, ordering), provider-provider relations were learned and used to build patient-specific provider networks representing the care coordination structure. We quantified the networks using standard network analysis metrics (e.g., in-degree, out-degree, betweenness centrality, and closeness centrality). Coordination structure effectiveness was measured as the association between the network metrics and PSLOS, as modeled by a proportional-odds, logistical regression model. The 18 provider networks exhibited various team compositions and various levels of structural complexity. Providers, whose patients had lower PSLOS, tended to disperse patient-related information to more colleagues within their network than those, who treated higher PSLOS patients (P = 0.0294). In the NICU, improved dissemination of information may be linked to reduced PSLOS. EHR data provides an efficient, accessible, and resource-friendly way to study care coordination using network analysis tools. This novel methodology offers an objective way to identify key performance and safety indicators of care coordination and to study dissemination of patient-related information within care provider networks and its effect on care. Findings should guide improvements in the EHR system design to facilitate effective clinical communications among providers.

Recent studies focused on the importance of adopting network analysis approaches such as social network analysis in the supply chain networks to better understand and manage the roles of organizations in inter-organizational relationships. The main aim of this research is to identify and integrate network analysis metrics in the existent literature in this realm which is applicable to characterize the position and role of organizations in the supply chain network context and their impact on the behavior and outcomes of organizations and the whole supply chain network. To this aim, we followed a systematic literature review process using Scopus database to identify high-quality papers through several screening stages. Our findings illustrate that there are two main sources of interfirm differences including atomistic properties and relational properties. With an emphasis on relational properties through the lens of network analysis metrics, we integrated influential characteristics on actor’s behavior and performance into three main categories of node level, tie level, and network level. Our findings are applicable to address any emergent phenomenon and the roles of actors based on their position in the network context such as supply chain network and study their behavior and performance.

In large-scale software systems that integrate many components originating from different vendors, the understanding of the functional importance of the components is critical for the dependability of the system. However, in general, gaining such understanding is difficult. Here we describe the application of the combination of dynamic analysis and network analysis to large-scale software systems with the aim to determine methods of classes that are functionally important with respect to a given functionality of the software. We use as a test case the Google Chrome and predict functionally important methods in a weak sense in the context of usage scenarios. We validate the predictions using mutation testing and evaluate the behavior of the software following the mutation change. Our results indicate that network analysis metrics based on measurement of structural integrity can be used to predict methods of classes that are functionally important with respect to a given functionality of the software system.

BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

This thesis explores the application of complex systems research to understand the dynamics of the art world, considering art as a complex system and investigating its various components through data-driven methodologies. By studying art as a complex system, we contribute to a systematic understanding of human development and behavior in creative domains. The social network plays a crucial role in the cultural evolution of art, shaping our cultural identity and collective memory. Analyzing network characteristics provides insights into how individual decisions influence collective dynamics and sustain social phenomena. Previous studies have used network models and data analysis to examine the role of network position and connectedness in artistic collaborations, individual success, and the transmission of artistic knowledge. In this thesis, we focus on ballet as a unique art form with a rich historical and social structure. Ballet provides an opportunity to investigate the role of the social network in shaping collective dynamics in performing arts. We present two research articles that examine gender inequalities and the role of social connections on the career success of ballet dancers. The first article investigates the social network structure of ballet creations and its potential impact on gender disparities in leading positions. The second article explores the influence of social connections and prestige on the career trajectories of ballet dancers, using network analysis and centrality metrics to uncover hierarchical stratification within ballet academies. Our research highlights the significance of social dynamics and network effects in understanding complex social phenomena in the art world. It offers insights into gender inequalities and career success in ballet and demonstrates the value of data-centric methodologies in arts research. By generating a unique dataset and applying interdisciplinary approaches, we contribute to the scientific examination of the arts and enhance our understanding of human creativity and cultural heritage. This thesis contributes to the broader goal of fostering diversity, equity, and inclusion within the arts by shedding light on social structures and suggesting potential avenues for change. Lastly, this work underscores the importance of interdisciplinary research in enriching our understanding of human development and behavior in creative domains.

Network science has become an invaluable tool for neuroimaging analysis and has advanced our understanding of the brain's complex structural and functional topology. Network science, an interdisciplinary offshoot of graph theory, conceives of the brain as a system of nodes, representing brain regions or voxels, and edges, representing the structural or functional connection between these regions or voxels. Such a model is considered appealing in neuroscience as it describes a system with various interacting regions that produces complex behaviors. This conception of the brain has revealed that the brain exhibits “small-world” properties, namely that brain networks are systems with regional specialization and efficient global information transfer. More importantly, studies in brain networks have furthered our understanding of diseases and disorders affecting the brain. Assessing group differences in representative samples of brain networks has become increasingly important, yet appropriate methodological developments have remained relatively sparse. Additionally, there is increased interest in understanding dynamic changes, or how a network changes over time. Currently, most network analysis tools are designed to analyze a static representation, thus changes that occur over time are often overlooked or ignored. As the field continues to grow, it is becoming increasingly important to develop methods for understanding the complexity of these data. This research topic features selected contributions that explores group analysis and dynamics in brain network studies including methodological and practical applications employing various imaging modalities. This snapshot of the field represents the growing interest to expand and validate new methods for use in the field of brain network neuroscience. “Defining nodes in complex brain networks” is a review article discussing the methodological choices for defining nodes, particularly how the choice of atlas-based or voxel-based methods affects the computed network (Stanley et al., 2013). “Routing in the brain” is a commentary on network routing focusing on how current methods and models may not be adequate for brain networks. Further exploration of the packet switching network model may be more realistic and appropriate for the brain (Graham, 2014). “A multimodal approach for determining brain networks by jointly modeling functional and structural connectivity” is a methods article introducing a Bayesian model for estimating functional networks, from functional magnetic resonance imaging (fMRI) data, leveraging complementary structural diffusion tensor imaging (DTI) data (Xue et al., 2015). “A permutation testing framework to compare groups of brain networks” is a methods article describing a permutation testing framework to statistically compare groups of functional brain networks while incorporating topological features inherent in each individual network (Simpson et al., 2013). “Assessing dynamics, spatial scale, and uncertainty in task-related brain network analyses” is a methods article describing a statistical approach for computing uncertainty in static and dynamic functional networks and aggregating network measures in task-related electrocorticography (ECoG) data (Stephen et al., 2014). “Hierarchical vector auto-regressive models and their applications to multi-subject effective connectivity” is a methods article introducing a generalization of the vector auto-regressive (VAR) model for comparing effective connectivity between experimental conditions while accounting for between-subject heterogeneity (Gorrostieta et al., 2013). “Statistical network analysis for functional MRI: summary networks and group comparisons” is a review article discussing the construction of summary networks and how to test for topological differences in groups of networks when these groups also exhibit significant differences in density (Ginestet et al., 2014). “Detecting functional connectivity change points for single-subject fMRI data” is a methods article that extends Dynamic Connectivity Regression (DCR), a data-driven technique which detects change points in functional connectivity between brain regions to determine state changes over the course of an experimental task, by introducing a novel algorithm aimed at increasing the estimation of networks for individual subject data (Cribben et al., 2013). “The Laplacian spectrum of neural networks” is an original research article examining the use of the Laplacian spectrum in anatomic networks of the macaque, cat and Caenorhabditis elegans. This method describes network structure at a systems level, assessing systemic infrastructural properties of the entire network as opposed to properties of specific nodes or connections (De Lange et al., 2014). “Quantifying network properties in multi-electrode recordings: spatiotemporal characterization and inter-trial variation of evoked gamma oscillations in mouse somatosensory cortex in vitro” is an original research article on network dynamics in multi-electrode array data focusing on changes in the functional connectivity of evoked gamma oscillations in cortical circuits (Carmeli et al., 2013).

Important author analysis is one of the key issues in the research professionals’ relationship network. Research professionals’ relationship network is a type of social network which is constitute of research professionals and there co-author relationship with other professionals. So many social network analysis metrics are available to analyze the important or prominent actor in the network. Centrality in social network analysis represents prestige or importance of a node with respect to other nodes in the network and also represents the importance of relationship between nodes. In this paper, we studied social network theory to understand how the collaboration of research professionals has impact in research world and performance of individual researcher. For this analysis, we use social network analysis metrics like normalize degree centrality, closeness centrality, betweenness centrality and eigenvector centrality.KeywordsSocial networkResearcher relationCentrality

<p>ORFEUS (Observatories and Research Facilities for European Seismology) is a non-profit foundation that promotes seismology in the Euro-Mediterranean area through the collection, archival and distribution of seismic waveform data, metadata and closely related products. The data and services are collected or developed at national level by more than 60 contributing Institutions in Pan-Europe and further developed, integrated, standardized, homogenized and promoted through ORFEUS. Among the goals of ORFEUS are: (a) the development and coordination of waveform data products; (b) the coordination of a European data distribution system, and the support for seismic networks in archiving and exchanging digital seismic waveform data; (c) the encouragement of the adoption of best practices for seismic network operation, data quality control and data management; (d) the promotion of open access to seismic waveform data, products and services for the broader Earth science community.  These goals are achieved through the development and maintenance of services targeted to a broad community of seismological data users, ranging from earth scientists to earthquake engineering practitioners. Two Service Management Committees (SMCs) are consolidated within ORFEUS devoted to managing, operating and developing (with the support of one or more Infrastructure Development Groups): (i) the European Integrated waveform Data Archive (EIDA; https://www.orfeus-eu.org/data/eida/); and (ii) the European Strong-Motion databases (SM; https://www.orfeus-eu.org/data/strong/). A new SMC is being formed to represent the community of European mobile pools. Products and services for computational seismologists are also considered for integration in the ORFEUS domain. ORFEUS services currently provide access to the waveforms acquired by ~ 10,000 stations in Pan-Europe, including dense temporary experiments, with strong emphasis on open, high-quality data. Contributing to ORFEUS data archives means long-term archival, state-of-the-art quality control, improved access and increased  usage. Access to data and products is ensured through state-of-the-art information and communications technologies, with strong emphasis on federated web services that considerably improve seamless user access to data gathered and/or distributed by ORFEUS institutions. The web services also facilitate the automation of downstream products. Particular attention is paid to adopting clear policies and licences, and acknowledging the crucial role played by data providers / owners, who are part of the ORFEUS community. There are significant efforts by ORFEUS participating Institutions to enhance the existing services to tackle the challenges posed by the Big Data Era, with emphasis on data quality, improved user experience, and implementation of strategies for scalability, high-volume data access and archival. ORFEUS data and services are assessed and improved through the technical and scientific feedback of a User Advisory Group (UAG), comprised of European Earth scientists with expertise encompassing a broad range of disciplines. All ORFEUS services are developed in coordination with EPOS and are largely integrated in the EPOS Data Access Portal. ORFEUS is one of the founding Parties and fundamental pillars of EPOS Seismology. This contribution presents the current products and services of ORFEUS and introduces the planned key future activities. We aim at stimulating Community feedback about the current and planned ORFEUS strategies.</p>

The rapid advancement of information and communication technology is now increasingly utilized by the public in all sectors of activity, one of which is archival data management. The importance of archival data management today, increases awareness in agencies and organizations by utilizing information technology. Conventional archival data management can result in the possibility of errors that can be caused by several factors, namely, the age of archival data storage, loss of archival data due to work negligence and damage to archival data. Therefore, the existence of a website-based information system that can be used by organizations that can minimize errors in archival data management. The purpose of this study is to design an information system with the implementation of the Levenshtein algorithm on zakat recipient archives at BAZNAS Tapanuli Tengah Regency, where the performance of the Levenshtein algorithm here functions to measure the difference or distance between two strings in order to be efficient in time when searching for stored archival data.

Grand challenges are system-wide problems. While top-down approaches to mitigating grand challenges may have potentially far-reaching impact, such initiatives from the higher scales can fail because of limited knowledge of ground realities. At the same time, resilient local efforts to address societal challenges can have limited reach. How can a grassroots organization work across locations and scales to address a grand challenge? We report the findings of a longitudinal case study of the Child In Need Institute (CINI), a non-profit organization that successfully scaled its impact to reach over seven million beneficiaries. Based on 40 years of archival data and four years of intensive fieldwork, our findings reveal “double weaving” as a recursive process of diagnosing and addressing problems by connecting actors and resources across locations and scales. The double-weaving process gives researchers a framework to explore the complex interplay between locations and scales that is necessary to address grand challenges. Our study contributes to research on grand challenges and scaling, advancing the spatial turn in organizational studies by developing the scalar dimension and expanding research that has primarily focused on locations.

Organizations can create volume flexibility—the ability to increase capacity up or down to meet demand for a single service—through the use of flexible labor resources (e.g., part-time and temporary workers, as compared to full-time workers). Although organizations are increasingly using these resources, the relationship between flexible labor resources and financial performance has not been examined empirically in the service setting. We use two years of archival data from 445 stores of a large retailer to study this relationship. We hypothesize and find that increasing the labor mix of temporary or part-time workers shows an inverted U-shaped relationship with sales and profit while temporary labor mix has a U-shaped relationship with expenses. Thus, although flexible labor resources can create volume flexibility for a firm along multiple dimensions, it is possible to have too much of a good thing.This paper was accepted by Serguei Netessine, operations management.

The human and economic costs of pollution and climate change are especially high for emerging markets; thus, the need to shift to sustainable technologies is essential and urgent. However, the development and commercialization of any new product requires an ecosystem consisting of resource providers such as investors, suppliers, complementors, favorable policy and regulations, and more importantly, a market for these products. How does such an ecosystem emerge, especially in an emerging market context where there are institutional gaps and even “normal” entrepreneurship is difficult? We investigate this question in the context of the Indian electric vehicle industry, using 40 years of archival data, 54 semi-structured interviews, and ethnographic field notes. Our findings suggest that institutional entrepreneurship is more complicated than previously imagined and involves multiple eras characterized by a distinct combination of technology, supplier and complementors, market and competition, capital, and regulation. Each era gives rise to a distinct type of entrepreneur (the Schumpeterian visionary, the Kerznerian opportunist, the Buchananian aesthete, and the Knightian pragmatist), and the actors in each era, often accidentally and unintentionally, create the conditions for the next, culminating over time in an electric vehicle ecosystem that can support viable businesses. We contribute to the literature on ecosystems by taking a longitudinal perspective and showing how ecosystems involving various actors emerge across multiple stages. We also adds to the literature on industry emergence by investigating an emerging market context where the chief source of uncertainty is not just technology but the interdependent and evolving uncertainty of regulation, market, capital and supplier.

Construction is considered to be one of the most dangerous industries due to its wide variety of accidents and high risk. Although people have been using various means to improve safety levels, the frequent occurrence of accidents still plagues the development of this industry. Accurately grasping the characteristics and analyzing the complex relationships between the risks of different accident types are the keys to a comprehensive understanding and promotion of construction safety. Simultaneously, we need to note that the level and frequency of accidents affects the risk characteristics, but previous studies have not paid sufficient attention to these features. Using complex network theory, based on the risk factors and risk triggering relationship extracted from construction accidents and considering the accident level and the frequency of occurrences as their weights, several weighted risk networks are generated according to different accident types. Then, several network analysis metrics are introduced to reveal the characteristics of the risk network and identify the key risk factors. The analytical framework helps to pose targeted suggestions to reduce the occurrence of frequent major accidents.

Processing of electroencephalography (EEG) data requires multiple steps to remove noise and artifacts and select good-quality data. While powerful open-source toolboxes like MNE-Python exist, their command-line nature can pose a barrier for researchers without programming experience. Here, we present EEG-Pype, an open-source (Apache-2.0 licensed) graphical user interface application using MNE-Python functions. EEG-Pype provides an intuitive workflow tailored for preprocessing of resting-state EEG data, including frequency band filtering, independent component analysis and atlas-based beamforming for source-level analysis. The application supports several common raw EEG input file formats and guides users through a comprehensive pipeline focused on manual bad channel and epoch selection. Manual steps are streamlined using MNE-Python's interactive plots, resulting in a user-friendly experience. Configuration saving and loading allows for batch (re)runs, while a separate log is also saved, improving reproducibility and documentation. Output can be saved after filtering in canonical frequency bands, ready for further analysis. EEG-Pype includes a module for calculating quantitative EEG measures on preprocessed data, including spectral, functional connectivity and network analysis metrics. This softwareaims to lower the entry barrier for standardized EEG preprocessing, promoting reproducible research practices among neuroscientists and clinicians without requiring programming knowledge. EEG-Pype can be downloaded from: https://github.com/yorbenlodema/EEG-Pype and is not dependent on a specific operating system.