Network Science Research Articles

Identifying interdisciplinary emergence in the science of science: combination of network analysis and BERTopic

Global scientific output is expanding exponentially, which in turn calls for a better understanding of the science of science and especially how the boundaries of scientific fields expand through processes of emergence. The present study proposes the application of embedded topic modeling techniques to identify new emerging science via knowledge recombination activities as evidenced through the analysis of research publication metadata. First, a dataset is constructed from metadata derived from the Web of Science Core Collection database. The dataset is then used to generate a global map representing a categorical scientific co-occurrence network. A research field is defined as interdisciplinary when multiple science categories are listed in its description. Second, the co-occurrence networks are subsequently compared between periods to determine changing patterns of influence in light of interdisciplinarity. Third, embedded topic modeling enables unsupervised association of interdisciplinary classification. We present the results of the analysis to demonstrate the emergence of global interdisciplinary sciences and further we perform qualitative validation on the results to identify what the sources of the emergent areas are. Based on these results, we discuss potential applications for identifying emergence through the merging of global interdisciplinary domains.

Identifying influential spreaders in complex network based on the node’s weight and spreading probability

Identifying influential spreaders is a crucial aspect of network science with various applications, including rumor control, viral marketing and epidemic spread limitation. Despite the availability of various methods for identifying these spreaders in complex networks, there remains a fundamental question regarding their accurate and discriminative identification. To address the issues and account for each node’s propagation ability, we propose an algorithm to identify influential spreaders based on the node’s weight and spreading probability (NWSP) for identifying influential spreaders. The effectiveness of the proposed method is evaluated using the Susceptible–Infected–Recovered (SIR) model, Kendall’s Tau ([Formula: see text]) and monotonicity. The proposed method is compared with several well-known metrics, including degree centrality, K-shell decomposition, betweenness centrality, closeness centrality, eigenvector centrality and the centrality method based on node spreading probability (SPC), in ten real networks. Experimental results demonstrate the superiority ability of the proposed algorithm to accurately and discriminatively identify influential spreaders.

THE SOUTH DOES ALSO EXIST: THE CONTINENTAL DRIFT DEBATE IN THE ACCOUNT OF THE SOUTH-AFRICAN PALEOBOTANIST EDNA PLUMSTEAD

ABSTRACT This paper analyzes a letter sent by the South African paleobotanist Edna Plumstead in 1982 to the US philosopher of science Henry Frankel, in answer to his questions about her involvement in the early debates on the concept of continental drift. Her response gave a biographical account of her life and explained in what context she first became acquainted with the themes that would become her life's work, and the scientific network of people who influenced her along the way. Beginning with her undergraduate years in the 1920s, Plumstead was aware that continental drift was being discussed in scientific circles in South Africa, earlier than in the northern hemisphere. This was followed by her more direct involvement in paleontological studies on fossils of the seed-plant Glossopteris, her important description of Glossopteris reproductive organs in direct attachment to the leaves, and how the distribution of these fossils contributed to the concept of Gondwanaland and the continental drift theory. These contributions, although extremely relevant to science, were mostly ignored in the Global North.

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.

Network science: Ising states of matter

Network science: Ising states of matter

THE OPPORTUNITIES, LIMITATIONS, AND CHALLENGES IN USING MACHINE LEARNING TECHNOLOGIES FOR HUMANITARIAN WORK AND DEVELOPMENT

Novel digital data sources and tools like machine learning (ML) and artificial intelligence (AI) have the potential to revolutionize data about development and can contribute to monitoring and mitigating humanitarian problems. The potential of applying novel technologies to solving some of humanity’s most pressing issues has garnered interest outside the traditional disciplines studying and working on international development. Today, scientific communities in fields like Computational Social Science, Network Science, Complex Systems, Human Computer Interaction, Machine Learning, and the broader AI field are increasingly starting to pay attention to these pressing issues. However, are sophisticated data driven tools ready to be used for solving real-world problems with imperfect data and of staggering complexity? We outline the current state-of-the-art and identify barriers, which need to be surmounted in order for data-driven technologies to become useful in humanitarian and development contexts. We argue that, without organized and purposeful efforts, these new technologies risk at best falling short of promised goals, at worst they can increase inequality, amplify discrimination, and infringe upon human rights.

The influence of collaborators and knowledge diversity on innovation performance of tourism and hospitality academia network

In the knowledge economy era, there is a growing global emphasis on the impact of international scientific collaboration networks (SCNs) on academic innovation. Based on a cross-level analysis perspective, this study constructs a dual-stage cross-level moderated mediation model and tests the hypotheses related to scientific collaboration, knowledge diversity, and innovation performance. The data were collected from the tourism and hospitality academia publications from 1983 to 2018, involving 79 groups of 303 academics from China. This study indicates that knowledge diversity positively mediates the relationship between the number of collaborators and tourism academics’ innovation performance. Furthermore, in complex SCNs, network relationship scale and team collaboration density have cross-level moderated effects on this process.

Dual-learning Multi-hop Nonnegative Matrix Factorization for community detection

As an important branch of network science, community detection has garnered significant attention. Among various community detection methods, nonnegative matrix factorization (NMF)-based community detection approaches have become a popular research topic. However, most NMF-based methods overlook the network’s multi-hop information, let alone the community detection results specific to each hop of the network. In this paper, we propose Dual-learning Multi-hop NMF (DL-MHNMF), a method that considers not only the multi-hop connectivity between two nodes but also factors in the shared results across multiple hops and the impact of differences in the specific results at each hop on the shared outcomes. An efficient iterative optimization algorithm with guaranteed theoretical convergence is proposed for solving DL-MHNMF. Methodologically, by iteratively removing the specific results during the optimization process of DL-MHNMF, we achieve enhanced detection accuracy, which is also verified by subsequent experiments. Specifically, we compare fourteen algorithms on eleven publicly available datasets, and experimental results show that our algorithm outperforms most state-of-the-art methods.

A network-based modeling framework reveals the core signal transduction network underlying high carbon dioxide-induced stomatal closure in guard cells.

Stomata are pores on plant aerial surfaces, each bordered by a pair of guard cells. They control gas exchange vital for plant survival. Understanding how guard cells respond to environmental signals such as atmospheric carbon dioxide (CO2) levels is not only insightful to fundamental biology but also relevant to real-world issues of crop productivity under global climate change. In the past decade, multiple important signaling elements for stomatal closure induced by elevated CO2 have been identified. Yet, there is no comprehensive understanding of high CO2-induced stomatal closure. In this work, we assemble a cellular signaling network underlying high CO2-induced stomatal closure by integrating evidence from a comprehensive literature analysis. We further construct a Boolean dynamic model of the network, which allows in silico simulation of the stomatal closure response to high CO2 in wild-type Arabidopsis thaliana plants and in cases of pharmacological or genetic manipulation of network nodes. Our model has a 91% accuracy in capturing known experimental observations. We perform network-based logical analysis and reveal a feedback core of the network, which dictates cellular decisions in closure response to high CO2. Based on these analyses, we predict and experimentally confirm that applying nitric oxide (NO) induces stomatal closure in ambient CO2 and causes hypersensitivity to elevated CO2. Moreover, we predict a negative regulatory relationship between NO and the protein phosphatase ABI2 and find experimentally that NO inhibits ABI2 phosphatase activity. The experimental validation of these model predictions demonstrates the effectiveness of network-based modeling and highlights the decision-making role of the feedback core of the network in signal transduction. We further explore the model's potential in predicting targets of signaling elements not yet connected to the CO2 network. Our combination of network science, in silico model simulation, and experimental assays demonstrates an effective interdisciplinary approach to understanding system-level biology.

Investigation of Indian stock markets using topological data analysis and geometry-inspired network measures

Financial markets are a quintessential example of complex systems wherein strategic connections arising through the trading (buying and selling) of different stocks gives rise to dynamic correlation patterns in stock prices. Network science based approaches have been employed for more than two decades to investigate the stock market as an inferential network, with nodes representing stocks and edges as pairwise cross-correlations between stock prices. However, a recent focus has been the characterization of the higher-order structure, beyond pairwise correlation, of such networks. To this end, geometry-inspired measures (such as discrete Ricci curvatures) and topological data analysis (TDA) based methods (such as persistent homology) have become attractive tools for characterizing the higher-order structure of networks representing the financial systems. In this study, our goal is to perform a comparative analysis of both these approaches, especially by assessing the fragility and systemic risk in the Indian stock markets, which is known for its high volatility and risk. To achieve this goal, we analyze the time series of daily log-returns of stocks comprising the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE). Specifically, our aim is to monitor the changes in standard network measures, edge-centric discrete Ricci curvatures, and persistent homology based topological measures computed from cross-correlation matrices of stocks. In this study, the edge-centric discrete Ricci curvatures have been employed for the first time in the analysis of the Indian stock markets. The Indian stock markets are known to be less diverse in comparison to the US market, and hence provides us an interesting example. Our results point that, among the persistent homology based topological measures, persistent entropy is simple and more robust than L1-norm and L2-norm of persistence landscape. In a broader comparison between network analysis and TDA, we highlight that the network analysis is sensitive to the way of constructing the networks (threshold or minimum spanning tree), as well as the threshold values used to construct the correlation-based threshold networks. On the other hand, the persistent homology is a more robust approach and is able to capture the higher-order interactions and eliminate noisy data in financial systems, since it does not take into account a single value of threshold but rather a range of values.

Enhancing Collaboration and Support for the French Scientific Diaspora in the United States

The French scientific diaspora in the United States (US) is a diverse community of professionals, essential for France and the host country. Their contribution to science and innovation, in both nations, stem from bilateral collaborations and knowledge exchange. Yet, while living abroad, this group of scientists faces multiple obstacles that can interfere with their productivity and influence. This report highlights the necessity of determining how numerous stakeholders can support and actively engage with this diaspora. To identify the needs of the French scientific community in the US, data was collected during seven interviews with diaspora network managers and analyzed various aspects defining the landscape of the community: the challenges and concerns it faces, the benefits of engaging in a diaspora network, and the opportunities to support these networks. These findings enumerate the difficulties encountered by the diaspora members and the networks themselves, ranging from lack of funding and administrative roadblocks to visibility and long-term sustainability issues. Moreover, the report underscores that involvement with other international diaspora networks considerably expands and diversifies professional connections, facilitating the exchange of experiences and best practices between networks. This analysis emphasizes the needs of the French scientific diaspora and the incentives and actions for supporting it, facilitating the establishment of a set of strategic recommendations for the European Union (EU), France, and the US. Furthermore, it proposes a series of measures to improve communication and collaboration, among French scientific diaspora networks and their international counterparts. Ultimately, by providing an in-depth comprehension of the French scientific diaspora in the US and establishing recommendations to foster these networks, this report paves the way for a more interconnected future, extending beyond the scientific sphere. Additionally, this study can serve as a guide for other scientific diasporas in the US or other regions of the world.

Hate-sharing: A case study of its prevalence and impact on Gab

This article brings frameworks from literary and cultural studies and methods from network science to bear on a central topic in political communication research: polarization. Recent studies have called into question the argument that digital “echo chambers” exacerbate polarization by preventing members from encountering a diversity of information and opinions. Using Gab, a far-right social media platform, as a case study, we offer further evidence that even members of highly polarized publics do engage in “cross-cutting.” However, we develop a distinct concept of hate-sharing, or sharing content for the purpose of disagreeing with or denigrating it. We show that hate-sharing is common on Gab. Moreover, it is associated with stronger community structure than other kinds of sharing and appears to confer substantially greater influence on those who engage in it. We interpret these findings as evidence that social networks incentivize the production of networked outrage—where “hating on” linked content merges with hate.

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.

Global scientific trends on the islet transplantation in the 21st century: A bibliometric and visualized analysis.

Islet transplantation (IT) has emerged as a significant research area for the treatment of diabetes mellitus and has witnessed a surge in scholarly attention. Despite its growing importance, there is a lack of bibliometric analyses that encapsulate the evolution and scientific underpinnings of this field. This study aims to fill this gap by conducting a comprehensive bibliometric analysis to delineate current research hotspots and forecast future trajectories within the IT domain with a particular focus on evidence-based medicine practices. This analysis scrutinized literature from January 1, 2000, to October 1, 2023, using the Web of Science Core Collection (WoSCC). Employing bibliometric tools such as VOSviewer, CiteSpace, and the R package "bibliometrix," we systematically evaluated the literature to uncover scientific trends and collaboration networks in IT research. The analysis revealed 8388 publications from 82 countries, predominantly the United States and China. However, global cross-institutional collaboration in IT research requires further strengthening. The number of IT-related publications has increased annually. Leading research institutions in this field include Harvard University, the University of Alberta, the University of Miami, and the University of Minnesota. "Transplantation" emerges as the most frequently cited journal in this area. Shapiro and Ricordi were the most prolific authors, with 126 and 121 publications, respectively. Shapiro also led to co-citations, totaling 4808. Key research focuses on IT sites and procedures as well as novel therapies in IT. Emerging research hotspots are identified by terms like "xenotransplantation," "apoptosis," "stem cells," "immunosuppression," and "microencapsulation." The findings underscore a mounting anticipation for future IT research, which is expected to delve deeper into evidence-based methodologies for IT sites, procedures, and novel therapeutic interventions. This shift toward evidence-based medicine underscores the field's commitment to enhancing the efficacy and safety of IT for diabetes treatment, signaling a promising direction for future investigations aimed at optimizing patient outcomes.

Who died, who survived? COVID-19 epidemic, industrial dynamics and urban economic resilience

The COVID-19 epidemic has altered the economic geographies of cities. However, the academic focus on urban industries and their structural dynamics is far from enough. In this paper, we integrate literature from economic geography, business studies and network sciences, and design an adaptation framework to theorize urban economic resilience. Taking the COVID-19 outbreak in Xinfadi, Beijing during the summer of 2020 as a case study, we use Logit regressions and DID estimations to identify the impacts of the epidemic on local industrial dynamics. Our results support that the COVID-19 outbreak significantly increases the number of business exits. The negative impacts are concentrated in high-risk regions, pointing to specific industries and individual-owned firms. Furthermore, we use a machine learning method to visualize the dynamics of industry networks in a small-scale urban area, and then simulate the transmission paths of industry-failure. In doing so, we contribute to current economic resilience literature that firm-level adaptation and industrial dynamics complement regional adaptation theory, while the cascading effects in the industry network underpin the micro foundations of resilience formation.

Scaling theory of fractal complex networks

We show that fractality in complex networks arises from the geometric self-similarity of their built-in hierarchical community-like structure, which is mathematically described by the scale-invariant equation for the masses of the boxes with which we cover the network when determining its box dimension. This approach—grounded in both scaling theory of phase transitions and renormalization group theory—leads to the consistent scaling theory of fractal complex networks, which complements the collection of scaling exponents with several new ones and reveals various relationships between them. We propose the introduction of two classes of exponents: microscopic and macroscopic, characterizing the local structure of fractal complex networks and their global properties, respectively. Interestingly, exponents from both classes are related to each other and only a few of them (three out of seven) are independent, thus bridging the local self-similarity and global scale-invariance in fractal networks. We successfully verify our findings in real networks situated in various fields (information—the World Wide Web, biological—the human brain, and social—scientific collaboration networks) and in several fractal network models.

Spatial Constraints on Economic Interactions: A Complexity Approach to the Japanese Inter-Firm Trade Network

The trade distance is an important constraining factor underpinning the emergence of social and economic interactions of complex systems. However, agent-based studies supported by the granular analysis of distances are limited. Here, we present a complexity method that places the actual geographical locations of individual firms in Japan at the epicentre of our research. By combining methods derived from network science together with information theory measures, and by using a comprehensive dataset of Japanese inter-firm business transactions, we evaluate the effects of spatial features on the structural patterns of the economy. We find that the normalised probability distributions of the distances between interacting firms obey a power law like decay concomitant with the sizes of firms and regions. Furthermore, small firms would reach large distances to become customers of large firms, while trading between either only small firms or only large firms tends to be at smaller distances. Furthermore, a time evolution analysis suggests a reduction in the overall average trading distances in last 20 years. Lastly, our analysis concerning the trading dynamics among prefectures indicates that the preference to trade with neighbouring prefectures tends to be more pronounced at rural regions as opposed to the larger central conurbations.

Multifaceted online coordinated behavior in the 2020 US presidential election

Organized attempts to manipulate public opinion during election run-ups have dominated online debates in the last few years. Such attempts require numerous accounts to act in coordination to exert influence. Yet, the ways in which coordinated behavior surfaces during major online political debates is still largely unclear. This study sheds light on coordinated behaviors that took place on Twitter (now X) during the 2020 US Presidential Election. Utilizing state-of-the-art network science methods, we detect and characterize the coordinated communities that participated in the debate. Our approach goes beyond previous analyses by proposing a multifaceted characterization of the coordinated communities that allows obtaining nuanced results. In particular, we uncover three main categories of coordinated users: (i) moderate groups genuinely interested in the electoral debate, (ii) conspiratorial groups that spread false information and divisive narratives, and (iii) foreign influence networks that either sought to tamper with the debate or that exploited it to publicize their own agendas. We also reveal a large use of automation by far-right foreign influence and conspiratorial communities. Conversely, left-leaning supporters were overall less coordinated and engaged primarily in harmless, factual communication. Our results also showed that Twitter was effective at thwarting the activity of some coordinated groups, while it failed on some other equally suspicious ones. Overall, this study advances the understanding of online human interactions and contributes new knowledge to mitigate cyber social threats.

Early career wins and tournament prestige characterize tennis players’ trajectories

Success in sports is a complex phenomenon that has only garnered limited research attention. In particular, we lack a deep scientific understanding of success in sports like tennis and the factors that contribute to it. Here, we study the unfolding of tennis players’ careers to understand the role of early career stages and the impact of specific tournaments on players’ trajectories. We employ a comprehensive approach combining network science and analysis of the Association of Tennis Professionals (ATP) tournament data and introduce a novel method to quantify tournament prestige based on the eigenvector centrality of the co-attendance network of tournaments. Focusing on the interplay between participation in central tournaments and players’ performance, we find that the level of the tournament where players achieve their first win is associated with becoming a top player. This work sheds light on the critical role of the initial stages in the progression of players’ careers, offering valuable insights into the dynamics of success in tennis.

Patterns of interactions among ICU interprofessional teams: A prospective patient-shift-level survey approach.

The Awakening, Breathing Coordination, Delirium monitoring and Early mobility bundle (ABCDE) is associated with lower mortality for intensive care unit (ICU) patients. However, efforts to improve ABCDE are variably successful, possibly due to lack of clarity about who are the team members interacting when caring for each patient, each shift. Lack of patient shift-level information regarding who is interacting with whom limits the ability to tailor interventions to the specific ICU team to improve ABCDE. Determine the number and types of individuals (i.e., clinicians and family members) interacting in the care of mechanically ventilated (MV) patients, as reported by the patients' assigned physician, nurse, and respiratory therapist (RT) each shift, using a network science lens. We conducted a prospective, patient-shift-level survey in 2 medical ICUs. For each patient, we surveyed the assigned physician, nurse, and RT each day and night shift about who they interacted with when providing ABCDE for each patient-shift. We determined the number and types of interactions, reported by physicians, nurses, and RTs and day versus night shift. From 1558 surveys from 404 clinicians who cared for 169 patients over 166 shifts (65% response rate), clinicians reported interacting with 2.6 individuals each shift (physicians: 2.65, nurses: 3.33, RTs: 1.86); this was fewer on night shift compared to day shift (1.99 versus 3.02). Most frequent interactions were with the bedside nurse, attending, resident, intern, and RT; family member interactions were reported in less than 1 in 5 surveys (12.2% of physician surveys, 19.7% of nurse surveys, 4.9% of RT surveys). Clinicians reported interacting with 3-4 clinicians each shift, and fewer on nights. Nurses interacted with the most clincians and family members. Interventions targeting shift-level teams, focusing on nurses and family members, may be a way to improve ABCDE delivery and ICU teamwork.