Network Science Research Research Articles

Robustness of Real-World Networks after Weight Thresholding with Strong Link Removal

Weight thresholding (WT) is a method intended to decrease the number of links within weighted networks that may otherwise be excessively dense for network science applications. WT aims to remove links to simplify the network by holding most of the features of the original network. Here, we test the robustness and the efficacy of the node attack strategies on real-world networks subjected to WT that remove links of higher weight (strong links). We measure the network robustness along node removal with the largest connected component (LCC). We find that the real-world networks under study are generally robust when subjected to WT. Nonetheless, WT with strong link removal changes the efficacy of the attack strategies and the rank of node centralities. Also, WT with strong link removal may trigger a more significant change in the node centrality rank than WT by removing weak links. Network science research with the aim to find important/influential nodes in the network has to consider that simplifying the network with WT methodologies may change the node centrality.

Coupled connectivity in the global complex network: the case of United Kingdom (1880–1925)

The main objective of this research is to analyse the connectivity of cities in a coupled network composed of planar (railways) and non-planar (maritime) topologies. It examines the state of the network during the 1880–1925 period, namely in the context of the first globalization wave (1880–1914), when trade and urban development were closely tied to progress in communications systems and steam propulsion especially. Edges represent intercity physical infrastructure on land, and inter-port ship voyages at sea. We tested several hypotheses in terms of inter-network specialisation and urban hierarchies with an application in the United Kingdom. The main results reveal that the networks are highly interdependent, whereas combined centrality is closely associated with city size and urban growth. We discuss the key results in light of network science, spatial science, maritime history, and transport research.

Exploring Consumer Hoarding Patterns: A Case Study Social Networks on Consumer Hoarding Behaviour of Toilet Paper

This article investigated the impact of social networks on consumer hoarding behavior during the COVID-19 pandemic, focusing specifically on the global toilet paper hoarding phenomenon. Motivated by the pandemic's widespread fear and uncertainty, the study filled a research gap by stressing the relevance of network science in understanding hoarding situations. The study, which examined a case study of the toilet paper scarcity that began in Hong Kong and extended globally, identified three major issues: the unique characteristics of online networks, the herding mentality promoted by social factors, and the asymmetry in information availability. To overcome these difficulties, the study suggests strategic options. It was suggested that artificial intelligence be used for content regulation and information validation in order to proactively counteract the unique features of internet networks. Targeted psychological interventions, notably stress reduction programs, were recommended to counteract the herding mindset that drives hoarding. To mitigate the impact of asymmetric information, media literacy initiatives aimed at improving consumers' critical thinking skills were suggested. Finally, this study gave important insights into the complex dynamics of social networks and consumer hoarding behavior during crises. It emphasized the importance of a comprehensive understanding of online networks' roles, laying the groundwork for future research in network science, consumer behavior, and crisis management.

Utilization of and perceived need for simulators in clinical electrophysiology: results from an EHRA physician survey.

Simulator training has been recently introduced in electrophysiology (EP) programmes in order to improve catheter manipulation skills without complication risks. The aim of this study is to survey the current use of EP simulators and the perceived need for these tools in clinical training and practice. A 20-item online questionnaire developed by the Scientific Initiatives Committee of the European Heart Rhythm Association (EHRA) in collaboration with EHRA Digital Committee was disseminated through the EHRA Scientific Research Network members, national EP groups, and social media platforms. Seventy-four respondents from 22 countries (73% males; 50% under 40 years old) completed the survey. Despite being perceived as useful among EP professionals (81%), EP simulators are rarely a part of the institutional cardiology training programme (20%) and only 18% of the respondents have an EP simulator at their institution. When available, simulators are mainly used in EP to train transseptal puncture, ablation, and mapping, followed by device implantation (cardiac resynchronization therapy [CRT], leadless, and conduction system pacing [CSP]). Almost all respondents (96%) believe that simulator programmes should be a part of the routine institutional EP training, hopefully developed by EHRA, in order to improve the efficacy and safety of EP procedures and in particular CSP 58%, CRT 42%, leadless pacing 38%, or complex arrhythmia ablations (VT 58%, PVI 45%, and PVC 42%). This current EHRA survey identified a perceived need but a lack of institutional simulator programme access for electrophysiologists who could benefit from it in order to speed up the learning curve process and reduce complications of complex EP procedures.

A dataset of carbon and water fluxes in the sample plots of natural regeneration at Puding Station of Guizhou Province (2015–2019)

Targeting the Shawan natural regeneration ecosystem (converted from farmland in 2010) in Puding County, we adopted the eddy covariance (EC) system to carry out long-term positioning observations of carbon and water fluxes during the natural regeneration of ecosystems after farmland conversion in karst areas. Puding Station belongs to the National Field Scientific Observation and Research Network and the China Ecosystem Research Network. Based on the data processing requirements of the ChinaFLUX, we compiled in this dataset the carbon and water fluxes as well as conventional meteorological data in Shawan from 2015 to 2019, including net ecosystem carbon exchange (NEE), ecosystem respiration (Re), gross primary productivity (GPP), latent heat flux (LE), sensible heat flux (H), air temperature, air relative humidity, wind speed, wind direction, total solar radiation, net radiation, precipitation and so on. This dataset can provide observational evidence for the study of the carbon sink capacity and water consumption characteristics of the ecosystem at the early stage of natural regeneration after the conversion of sloping farmland to forest in karst areas in southern China, as well as their response to climate change.

Persian Medicine Network (PMN) Establishment in the Universal Scientific Education and Research Network (USERN).

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Relational event models in network science

AbstractRelational event models (REMs) for the analysis of social interaction were first introduced 15 years ago. Since then, a number of important substantive and methodological contributions have produced their progressive refinement and hence facilitated their increased adoption in studies of social and other networks. Today REMs represent a well-established class of statistical models for relational processes. This special issue of Network Science demonstrates the standing and recognition that REMs have achieved within the network analysis and networks science communities. We wrote this brief introductory editorial essay with four main objectives in mind: (i) positioning relational event data and models in the larger context of contemporary network science and social network research; (ii) reviewing some of the most important recent developments; (iii) presenting the innovative studies collected in this special issue as evidence of the empirical value of REMs, and (iv) identifying open questions and future research directions.

Considering weights in real social networks: A review

Network science offers powerful tools to model complex social systems. Most social network science research focuses on topological networks by simply considering the binary state of the links, i.e., their presence or absence. Nonetheless, complex social systems present heterogeneity in link interactions (link weight), and accounting for this heterogeneity, it is mandatory to design reliable social network models. Here, we revisit the topic of weighted social networks (WSNs). By summarizing the main notions, findings, and applications in the field of WSNs, we outline how WSN methodology may improve the modeling of several real problems in social sciences. We are convinced that WSNs may furnish ideas and insights to open interesting lines of new research in the social sciences.

A comprehensive approach for discrete resilience of complex networks.

The research and use of the term resilience in various types of technological, physiological, and socioeconomic systems has become very topical in recent years since this term has been applied in different fields with different meanings and connotations. One of the most common meanings of resilience is related to a positive idea that addresses recovery from failures. This study proposes to establish a theoretical and mathematical framework for discrete resilience that allows different systems to be quantitatively compared from this point of view. Also, a definition and a local view of the concept of resilience applicable to different characteristic measures in the field of complex networks is provided. Furthermore, several computational experiments are presented on the values of this new parameter in different types of synthetic and real-world networks, supplying a new set of conceptual tools for network science research.

Author multidisciplinarity and disciplinary roles in field of study networks

When studying large research corpora, “distant reading” methods are vital to understand the topics and trends in the corresponding research space. In particular, given the recognised benefits of multidisciplinary research, it may be important to map schools or communities of diverse research topics, and to understand the multidisciplinary role that topics play within and between these communities. This work proposes Field of Study (FoS) networks as a novel network representation for use in scientometric analysis. We describe the formation of FoS networks, which relate research topics according to the authors who publish in them, from corpora of articles in which fields of study can be identified. FoS networks are particularly useful for the distant reading of large datasets of research papers when analysed through the lens of exploring multidisciplinary science. In an evolving scientific landscape, modular communities in FoS networks offer an alternative categorisation strategy for research topics and sub-disciplines, when compared to traditional prescribed discipline classification schemes. Furthermore, structural role analysis of FoS networks can highlight important characteristics of topics in such communities. To support this, we present two case studies which explore multidisciplinary research in corpora of varying size and scope; namely, 6323 articles relating to network science research and 4,184,011 articles relating to research on the COVID-19-pandemic.

An Improved Gray Wolf Optimization Algorithm with a Novel Initialization Method for Community Detection

Community discovery (CD) under complex networks is a hot discussion issue in network science research. Recently, many evolutionary methods have been introduced to detect communities of networks. However, evolutionary optimization-based community discovery still suffers from two problems. First, the initialization population quality of the current evolutionary algorithm is not good, resulting in slow convergence speed, and the final performance needs to be further improved. Another important issue is that current methods of CD have inconsistent network detection performance at different scales, showing a dramatic drop as the network scale increases. To address such issues, this paper proposes an algorithm based on the novel initial method and improved gray wolf optimization (NIGWO) to tackle the above two problems at the same time. In this paper, a novel initialization strategy is proposed to generate a high-quality initial population and greatly accelerate the convergence speed of population evolution. The strategy effectively fused the elite substructure of the community and different features based on the dependency and other features among nodes. Moreover, an improved GWO is presented with two new search strategies. An improved hunting prey stage is proposed to retain the excellent substructures of populations and quickly improve the community structure. Furthermore, new mutation strategies from node level to community level are designed in an improved encircling prey stage. Specifically, boundary nodes are mutated according to a proposed function to improve the search efficiency and save the computation assumption. Numerous experiments have proven our method obtains more excellent performance in most networks compared with 11 state-of-the-art algorithms.

Trends in Naegleria fowleri global research: A bibliometric analysis study

Naegleria fowleri is one of the most dangerous protozoan agents. This article describes a bibliometric review of the literature on N. fowleri research indexed in WoS during a 51-year period. The VOSviewer visualization methodology was used to conduct a bibliometric study. The data included articles from the Web of Science database, nations, institutions, journals, keywords, co-authorship, co-citations, international collaborations, and citation rates. A total of 1106 articles were retrieved from the Web of Science database. The articles were cited 21,904 times in total (cited 12,138 times without self-citations). The average citation per article was 19.82. The Hirsch index was 63. The leading country according to the number of published articles was the United States of America (USA) (n = 447; 40.416%), followed by Mexico (n = 80; 7.233%), and Australia (n = 63; 5.696%). Other than these top three countries, the publications were from 74 countries globally. Especially after the 2000s, both the number of citations and the number of publications exhibited an increasing trend. The Virginia Commonwealth University (USA) (9.584%), Centers for Disease Control Prevention (USA) (8.770%), and Instituto Politecnico Nacional Mexico (4.069%) were the leading affiliations. Most of the leading affiliations were from the USA and Mexico. In conclusion, a bibliometric evaluation of N. fowleri was performed for the first time. Authors affiliated with institutions in the USA and Mexico have led scientific production on PAM. Efforts should be made to help developing countries with the highest prevalence of N. fowleri to develop scientific research networks with the USA and/or Mexico in order to increase research with interdisciplinary teams.

Networks for Science-Informed Innovation in the Arctic: Insights on the Structure and Evolution of a Canadian Research Network

In remote peripheral regions like the Arctic, research networks have been identified as an important mechanism for nurturing science-informed innovation. Given that relatively little is known about the network structures that support Arctic innovation processes, we employ social network analysis techniques to examine the structural organization and evolution of ArcticNet, a large Canadian Arctic scientific research network over a 13-year period (2004 – 17). ArcticNet funded 152 multidisciplinary research teams, connecting multiple types of science-based innovation actors, not including students (301 organizations and 1659 individuals). The research network grew without reaching saturation (increasing size, decreasing density), suggesting that ArcticNet was successful in recruiting new actors over the 13-year period. ArcticNet was centralized around non-local, public-sector actors (mainly Canadian academics). The emergence of collaborations across several boundaries (sectoral, geographic, thematic) suggests that non-local Canadian academic actors played an important boundary-spanning role, particularly in the early stages of the network. Participation by local northern actors doubled from Phase 1 to Phase 4, and with time, local northern actors had an increasing propensity for carrying out boundary-spanning roles and addressing structural holes. This study presents new insights into the networked nature of Arctic scientific research with potential implications for future research and innovation policy.

Editorial special issue ‘Public Pedagogy and Sustainability Challenges’

This special issue is developed within the scientific research network ‘Public Pedagogy and Sustainability Challenges’ funded by the Research Foundation Flanders (FWO). The network has brought together educational theorists, sustainability education researchers, sustainability transition researchers, artists and activists from Belgium, Sweden, Denmark, Poland, Ireland, Norway and South Africa. They share an interest in the relation between education and societal transformation and wanted to deepen and widen the understanding of the public role of education in the face of sustainability challenges through interdisciplinary research collaboration. The aim of the network and, hence, the special issue is to progress theory development and research on public pedagogy with a focus on sustainability challenges. It addresses issues such as how education can play a democratic role in addressing sustainability challenges, the conditions and obstacles for that, implications for the design of sustainability education practices, and theoretical, methodological and empirical implications for researching sustainability education as public pedagogy.

SARS-CoV-2 Dissemination Using a Network of the US Counties

During 2020 and 2021, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been increasing among the world’s population at an alarming rate. Reducing the spread of SARS-CoV-2 and other diseases that are spread in similar manners is paramount for public health officials as they seek to effectively manage resources and potential population control measures such as social distancing and quarantines. By analyzing the US county network structure, one can model and interdict potential higher infection areas. County officials can provide targeted information, preparedness training, and increase testing the researchers conclude that traditional the researchers conclude that traditional in these areas. While these approaches may provide adequate countermeasures for localized areas, they are inadequate for the holistic USA. We solve this problem by collecting coronavirus disease 2019 (COVID-19) infections and deaths from the Center for Disease Control and Prevention, and adjacency between all counties obtained from the United States Census Bureau. Generalized network autoregressive (GNAR) time series models have been proposed as an efficient learning algorithm for networked datasets. This work fuses network science and operations research techniques to univariately model COVID-19 cases, deaths, and current survivors across the US county network structure.

Principles for responsible management education - PRME: Collaboration among researchers

This study characterizes as a systematically reviews literature on the Principles for Responsible Management Education (PRME) through bibliometric research. The research maps and analyzes scientific research networks of PRME by identifying the distribution of publications per year, and delineating the most cited documents, along with co-authorship networks, country co-authorship, co-authorship networks among universities, and keyword occurrence across articles. The review examines 135 articles published in 50 journals by 304 authors affiliated with 187 institutions in 35 countries. Results highlight two specific approaches that recur frequently: first entails focusing descriptively on the application of PRME signatory institutions; second comprises the contribution of PRME to supporting signatories’ institutions to adopt an interdisciplinary approach for sustainability and corporate social responsibility (CSR) for responsible management education (RME). Moreover, the most-cited papers consider PRME in terms of dynamics, relation to business schools, and implementation regarding Sustainable Development Goals (SDGs). In terms of co-authorship networks, 80% of scientific output related to PRME has involved collaboration among researchers at various levels, reaching five continents and the study found fragmented networks with potential to achieve more density with greater partnership. The most-used keywords highlight future directions to raise the sustainability knowledge level, CSR, and SDG implementation to enrich RME.

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.

Networks in Learning Analytics: Where Theory, Methodology, and Practice Intersect

Network analysis has contributed to the emergence of learning analytics. In this editorial, we briefly introduce network science as a field and situate it within learning analytics. Drawing on the Learning Analytics Cycle, we highlight that effective application of network science methods in learning analytics involves critical considerations of learning processes, data, methods and metrics, and interventions, as well as ethics and value systems surrounding these areas. Careful work must meaningfully situate network methods and interventions within the theoretical assumptions explaining learning, as well as within pedagogical and technological factors shaping learning processes. The five empirical papers in the special section demonstrate diverse applications of network analysis, and the invited commentaries from cognitive network science and physics education research further discuss potential synergies between learning analytics and other sister fields with a shared interest in leveraging network science. We conclude by discussing opportunities to strengthen the rigour of network-based learning analytics projects, expand current work into nascent areas, and achieve more impact by holistically addressing the full cycle of learning analytics.

Cognitive cascades: How to model (and potentially counter) the spread of fake news.

Understanding the spread of false or dangerous beliefs-often called misinformation or disinformation-through a population has never seemed so urgent. Network science researchers have often taken a page from epidemiologists, and modeled the spread of false beliefs as similar to how a disease spreads through a social network. However, absent from those disease-inspired models is an internal model of an individual's set of current beliefs, where cognitive science has increasingly documented how the interaction between mental models and incoming messages seems to be crucially important for their adoption or rejection. Some computational social science modelers analyze agent-based models where individuals do have simulated cognition, but they often lack the strengths of network science, namely in empirically-driven network structures. We introduce a cognitive cascade model that combines a network science belief cascade approach with an internal cognitive model of the individual agents as in opinion diffusion models as a public opinion diffusion (POD) model, adding media institutions as agents which begin opinion cascades. We show that the model, even with a very simplistic belief function to capture cognitive effects cited in disinformation study (dissonance and exposure), adds expressive power over existing cascade models. We conduct an analysis of the cognitive cascade model with our simple cognitive function across various graph topologies and institutional messaging patterns. We argue from our results that population-level aggregate outcomes of the model qualitatively match what has been reported in COVID-related public opinion polls, and that the model dynamics lend insights as to how to address the spread of problematic beliefs. The overall model sets up a framework with which social science misinformation researchers and computational opinion diffusion modelers can join forces to understand, and hopefully learn how to best counter, the spread of disinformation and "alternative facts."

Topological analysis of the latent geometry of a complex network.

Most real-world networks are embedded in latent geometries. If a node in a network is found in the vicinity of another node in the latent geometry, the two nodes have a disproportionately high probability of being connected by a link. The latent geometry of a complex network is a central topic of research in network science, which has an expansive range of practical applications, such as efficient navigation, missing link prediction, and brain mapping. Despite the important role of topology in the structures and functions of complex systems, little to no study has been conducted to develop a method to estimate the general unknown latent geometry of complex networks. Topological data analysis, which has attracted extensive attention in the research community owing to its convincing performance, can be directly implemented into complex networks; however, even a small fraction (0.1%) of long-range links can completely erase the topological signature of the latent geometry. Inspired by the fact that long-range links in a network have disproportionately high loads, we develop a set of methods that can analyze the latent geometry of a complex network: the modified persistent homology diagram and the map of the latent geometry. These methods successfully reveal the topological properties of the synthetic and empirical networks used to validate the proposed methods.