Network Science Research Articles

Guiding prevention initiatives by applying network analysis to systems maps of adverse childhood experiences and adolescent suicide

Abstract Suicide is a leading cause of death in the United States, particularly among adolescents. In recent years, suicidal ideation, attempts, and fatalities have increased. Systems maps can effectively represent complex issues such as suicide, thus providing decision-support tools for policymakers to identify and evaluate interventions. While network science has served to examine systems maps in fields such as obesity, there is limited research at the intersection of suicidology and network science. In this paper, we apply network science to a large causal map of adverse childhood experiences (ACEs) and suicide to address this gap. The National Center for Injury Prevention and Control (NCIPC) within the Centers for Disease Control and Prevention recently created a causal map that encapsulates ACEs and adolescent suicide in 361 concept nodes and 946 directed relationships. In this study, we examine this map and three similar models through three related questions: (Q1) how do existing network-based models of suicide differ in terms of node- and network-level characteristics? (Q2) Using the NCIPC model as a unifying framework, how do current suicide intervention strategies align with prevailing theories of suicide? (Q3) How can the use of network science on the NCIPC model guide suicide interventions?

Distinct Network Patterns Emerge from Cartesian and XOR Epistasis Models: A Comparative Network Science Analysis.

Epistasis, the phenomenon where the effect of one gene (or variant) is masked or modified by one or more other genes, can significantly contribute to the observed phenotypic variance of complex traits. To date, it has been generally assumed that genetic interactions can be detected using a Cartesian, or multiplicative, interaction model commonly utilized in standard regression approaches. However, a recent study investigating epistasis in obesity-related traits in rats and mice has identified potential limitations of the Cartesian model, revealing that it only detects some of the genetic interactions occurring in these systems. By applying an alternative approach, the exclusive-or (XOR) model, the researchers detected a greater number of epistatic interactions and identified more biologically relevant ontological terms associated with the interacting loci. This suggests that the XOR model may provide a more comprehensive understanding of epistasis in these species and phenotypes. To further explore these findings and determine if different interaction models also make up distinct epistatic networks, we leverage network science to provide a more comprehensive view into the genetic interactions underlying BMI in this system. Our comparative analysis of networks derived from Cartesian and XOR interaction models in rats (Rattus norvegicus) uncovers distinct topological characteristics for each model-derived network. Notably, we discover that networks based on the XOR model exhibit an enhanced sensitivity to epistatic interactions. This sensitivity enables the identification of network communities, revealing novel trait-related biological functions through enrichment analysis. Furthermore, we identify triangle network motifs in the XOR epistatic network, suggestive of higher-order epistasis, based on the topology of lower-order epistasis. These findings highlight the XOR model's ability to uncover meaningful biological associations as well as higher-order epistasis from lower-order epistatic networks. Additionally, our results demonstrate that network approaches not only enhance epistasis detection capabilities but also provide more nuanced understandings of genetic architectures underlying complex traits. The identification of community structures and motifs within these distinct networks, especially in XOR, points to the potential for network science to aid in the discovery of novel genetic pathways and regulatory networks. Such insights are important for advancing our understanding of phenotype-genotype relationships.

Bakry–Émery–Ricci curvature: an alternative network geometry measure in the expanding toolbox of graph Ricci curvatures

Abstract The characterization of complex networks with tools originating in geometry, for instance through the statistics of so-called Ricci curvatures, is a well established tool of network science. Various types of such Ricci curvatures capture different aspects of network geometry. In the present work, we investigate Bakry–Émery–Ricci curvature, a notion of discrete Ricci curvature that has been studied much in geometry, but so far has not been applied to networks. We explore on standard classes of artificial networks as well as on selected empirical ones to what the statistics of that curvature are similar to or different from that of other curvatures, how it is correlated to other important network measures, and what it tells us about the underlying network. We observe that most vertices typically have negative curvature. Furthermore, the curvature distributions are different for different types of model networks. We observe a high positive correlation between Bakry–Émery–Ricci and both Forman–Ricci and Ollivier–Ricci curvature, and in particular with the augmented version of Forman–Ricci curvature while comparing them for both model and real-world networks. We investigate the correlation of Bakry–Émery–Ricci curvature with degree, clustering coefficient and vertex centrality measures. Also, we investigate the importance of vertices with highly negative curvature values to maintain communication in the network. Additionally, for Forman–Ricci, Augmented Forman–Ricci and Ollivier–Ricci curvature, we compare the robustness of the networks by comparing the sum of the incident edges and the minimum of the incident edges as vertex measures and find that the sum identifies vertices that are important for maintaining the connectivity of the network. The computational time for Bakry–Émery–Ricci curvature is shorter than that required for Ollivier–Ricci curvature but higher than for Augmented Forman–Ricci curvature. We therefore conclude that for empirical network analysis, the latter is the tool of choice.

Key nodes of misinformation source inference: A message-passing-based approach

The misinformation spreading in social networks causes unpredictable damage to the networked system, thus inferring the misinformation source is an important research topic in the field of network science and security. Many source inference algorithms have been proposed to find the most likely propagation source through observable snapshot. However, under limited observable conditions, observing different nodes states markedly affects the algorithm’s effectiveness. Yet, we still lack relevant research on which nodes can more accurately assist us in completing source inference. Here, we propose the heuristic message-passing-based algorithm to find the key nodes that can maximize the accuracy of source inference, which uses the average rank of the source in the message-passing method as a measure and performs continuous annealing on this basis to update the set. As a comparison, we propose random selection algorithm as the basic, high-eigenvalue algorithm and high-degree algorithm focused on centrality, and basic message-passing-based algorithm from the perspective of energy entropy in message passing. Through extensive numerical simulation on artificial and real-world networks, compared with other four algorithms, our heuristic message-passing-based algorithm finds the optimal key node set that can more accurately complete source inference. Moreover, it has over 8% higher inference accuracy than other methods in low visibility situations especially.

Mapping analytical methods between input–output economics and network science

AbstractThe input–output (IO) model can be used to examine the flow of products and services within an economy, resembling a network with industries as nodes and transactions as links. Diverging significantly from commonly studied networks such as social, protein, and power grids, IO networks exhibit intricate interconnectivity, involving weighted nodes and both directional and weighted links. This uniqueness necessitates careful consideration when applying complex network analysis techniques to IO systems. We critically review current complex network metrics and attempt to link them with existing IO approaches. Based on our assessment, certain network metrics, such as degree centrality and eigenvector centrality, have been explicitly integrated into the IO theory. In contrast, there exist metrics whose definitions and interpretations expand when applied in the context of IO analysis, including closeness and betweenness centrality. Additionally, network metrics are usually used to study topological features, identify key sectors, and construct novel metrics to study related issues. Network metrics used in IO analysis can identify important driver and transmission sectors in resource flow and environmental emission network, facilitating the development of targeted and reliable strategies. Besides, network metrics are used to quantify topological features and structural changes of the IO network which help strengthen the supply chain and mitigate both direct and indirect impacts of disruptions. Our ultimate goal is to establish connections and offer a roadmap for developing network‐based tools in IO analysis.

Этика – война – медицина: научный портрет проблемы в современных публикациях

Background: The search for the meaning of war, the justification of violence, the acceptance of guilt and other moral problems worried Russian thinkers of the 19th century. Russian religious philosophy considers morally justified only war in defense of the highest spiritual shrines, which I.A. Ilyin, N.A. Berdyaev, L.P. Karsavin, A.A. Kersnovsky and others call the defense of the home-land, life and peace. Aim: To assess research interest in the problems of ethics of war, ethics of military medicine, biomedical ethics in the intellectual field of open databases of scientific publications and offer a predictive plot of this scientific direction. Materials and methods: A comparative analysis of open databases of scientific publications RSCI, NEB, CyberLeninka was carried out. The study of literature sources in the problem field under consideration made it possible to determine the directions of the research search using vectors – keywords "ethics", "war", "military medicine". Publications from the open CyberLeninka data-base were used and analyzed as a model scientific network database. Quantitative (number of publications) and qualitative (annota-tions) indicators were studied. To identify attitudes towards the ethical problems of war, a sociological survey of medical university students was conducted, N = 154, average age of respondents (19.2 ± 1.9) years. Standards of confidentiality and autonomy were respected for respondents. Results: An analysis of the distribution of scientific publications by area of the international OECD classifier demonstrates the in-terest of researchers in the problem field of "Philosophy, ethics, religious studies" in 2020–2024. In all search options (using the key-words "War and Ethics", "War and Medicine", "Military Medicine") there are a larger number of publications in this area than in other social or sociological sciences. In the search query "war and ethics", there were 2 times more such publications than others. A pilot sur-vey of future doctors confirms the interdisciplinary connection between the ethics of war and medicine. Conclusion: The study demonstrates modern scientific interest in the ethics of war when studying problems related to war and mili-tary medicine. It would be of interest to conduct a more detailed study of the relationship between the ethics of war and medical ethics (bio-ethics) as an element of the ethical training of future doctors for professional activities in extreme conditions of peace and war.

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.

Partner violence in healthcare settings on breastfeeding mothers: a scientometric analysis

Background Partner violence is a significant issue affecting women’s health, particularly their breastfeeding practices in healthcare settings. Despite a considerable body of research on this topic, there is a need for a systematic overview of the existing literature to explore its thematic and structural developments. Methods This scientometric analysis covers a period of 45 years (1977-2022) and identifies 415 peer-reviewed articles published in English before 2023 from the Scopus database. The articles were included if they engaged with themes related to partner violence, healthcare settings, breastfeeding, and mothers, using predefined search terms and filters. Bibliometric data were decoded and visualized using VOSviewer and RStudio to analyze and visualize the scientific dialogue, trends, patterns, and networks in the literature regarding partner violence and its impact on breastfeeding mothers in healthcare settings. Results The study revealed a concentrated exploration of themes related to violence, healthcare provision, mental health, and women’s reproductive health. The co-occurrence network and thematic mapping illuminated seven pivotal clusters, revealing a complex narrative of interconnected research areas. Niche and emerging themes such as “Depression,” “HIV,” and “Covid-19” were identified, highlighting the evolving landscape of the research domain. Conclusions This analysis provides a panoramic overview of the existing literature, revealing intricate thematic structures and collaboration networks within the research on partner violence and breastfeeding mothers in healthcare settings. The study not only sheds light on the historical and current states of research but also pinpoints potential future directions, thereby assisting scholars and policymakers in navigating and prioritizing subsequent investigative endeavors.

CHORUS: A New Dataset of State Interest Group Policy Positions in the United States

Abstract Research on the activities and influence of interest groups in state legislatures faces a data problem: we are missing a comprehensive, systematic dataset of interest groups’ policy preferences on state legislation. We address this gap by introducing the Dataset on Policy Choice and Organizational Representation in the United States (CHORUS). This dataset compiles over 13 million policy positions stated by tens of thousands of interest groups and individuals on bills in 17 state legislatures over the past 25 years. We describe the process used to construct CHORUS and present a new network science technique for analyzing policy position data from interest groups: the layered stochastic block model, which groups similar interest groups and bills together, respectively, based on patterns in the policy positions. Through two demonstrative applications, we show the utility of these data, combined with our novel analytical approach, for understanding interest group configurations in different state legislatures and policy areas.

Spatial response of urban land use intensity to ecological networks: a case study of Xi'an Metropolitan Region, China.

In the face of the persistent degradation of ecological environments and fragmentation of ecological networks brought about by rapid urbanization, this study focuses on examining the interaction between urban land use intensity and ecological networks in the Xi'an Metropolitan Region (XAMR), China, and their impact on ecological equilibrium and sustainable development. By comprehensively evaluating the changes in land use intensity in XAMR from 2010 to 2020, the aim is to underscore the pivotal role of ecological networks in maintaining urban ecological balance and promoting sustainable development. The findings indicate a transition in land use intensity in the XAMR from low to high concentration, reflecting an intensification in land resource utilization during urbanization. However, the establishment and management of ecological networks can significantly enhance urban ecological security and biodiversity. Notably, this research identified crucial ecological corridors and source areas, augmenting the connectivity of urban green infrastructure and providing vital support for urban biodiversity. Additionally, a significant finding of this study is the spatial spillover effects generated by socioeconomic factors such as the proportion of tertiary and secondary industries and per capita GDP through the ecological network, which have profound impacts on land use intensity in the surrounding areas. These insights offer a novel understanding of the complex interactions within urban ecosystems, emphasizing the importance of incorporating ecological network construction in urban planning. Overall, through a comprehensive analysis of the relationship between the ecological network and land use intensity in the XAMR, this study proposes new directions for urban ecosystem management and land use planning, highlighting the significance of scientific ecological network planning and management in achieving long-term sustainable development in urbanization processes.

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.

Use of gabapentin for neuropathic pain therapy: A view from perspective of evidence-based medicine

The aim of the study was to analyze the literature sources for pharmacodynamic and pharmacokinetic features of gabapentin, providing its use in patients with neuropathic pain, as well as a comparative evaluation of its efficacy and safety when used in different doses.Materials and methods. PubMed, Google Scholar, EMBASE, ResearchGate scientific information network and elibrary.ru databases were used as search resources. The keywords used for the search were “gabapentin”, “mechanism of action”, “gabapentin targets”, “gabapentin pharmacodynamics”, “pharmacokinetics”, “pharmacokinetic parameters”, “neuropathic pain”, and “randomized clinical trials”. The depth of the search was 26 years (from 1998 to 2024). This review resulted in 87 literature sources.Results. Neuropathic pain (NeP) is one of the most common types of chronic pain, characterized by a high prevalence among people of the working age. Effective pharmacotherapy aimed at eliminating the pain syndrome is a key tool for improving the quality of life and preserving the work capacity of patients. Heterogeneity of etiologic factors involved in the genesis of NeP indicates the need to use drugs the analgesic effect of which is based on weakening the transmission of pain impulses in the CNS. In clinical trials, gabapentin has demonstrated efficacy in reducing the severity of pain in patients with postherpetic NeP, painful diabetic neuropathy and many other conditions accompanied by NeP. The dose of gabapentin 300 mg/day is the initial dose in the therapy of NeP and requires a further slow titration depending on the patient’s response to therapy and tolerability of the drug, especially in elderly and senile patients, as well as in patients with an impaired renal function. According to the published data, the most pronounced analgesic effect is achieved in the patients against the background of the gabapentin administration at a dose of 3600 mg/day.Conclusion. Gabapentin is the drug of choice in the management of patients with NeP of different etiology and intensity. A satisfactory safety profile and pharmacodynamic effects make gabapentin possible, despite the long history of its use, to remain a relevant drug used by a wide range of physicians, specialties, for pharmacotherapy of NeP patients.

Distinguishing subsampled power laws from other heavy-tailed distributions.

Distinguishing power-law distributions from other heavy-tailed distributions is challenging, and this task is often further complicated by subsampling effects. In this work, we evaluate the performance of two commonly used methods for detecting power-law distributions-the maximum likelihood method of Clauset etal. and the extreme value method of Voitalov etal.-in distinguishing subsampled power laws from two other heavy-tailed distributions, the lognormal and the stretched exponential distributions. We focus on a random subsampling method commonly applied in network science and biological sciences. In this subsampling scheme, we are ultimately interested in the frequency distribution of elements with a certain number of constituent parts-for example, species with k individuals or nodes with k connections-and each part is selected to the subsample with an equal probability. We investigate how well the results obtained from low-subsampling-depth subsamples generalize to the original distribution. Our results show that the power-law exponent of the original distribution can be estimated fairly accurately from subsamples, but classifying the distribution correctly is more challenging. The maximum likelihood method falsely rejects the power-law hypothesis for a large fraction of subsamples from power-law distributions. While the extreme value method correctly recognizes subsampled power-law distributions with all tested subsampling depths, its capacity to distinguish power laws from the heavy-tailed alternatives is limited. However, these false positives tend to result not from the subsampling itself but from the estimators' inability to classify the original sample correctly. In fact, we show that the extreme value method can sometimes be expected to perform better on subsamples than on the original samples from the lognormal and the stretched exponential distributions, while the contrary is true for the main tests included in the maximum likelihood method.

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 provides very powerful tools for extracting information from interacting data. Although recently the unsupervised detection of phases of matter using machine learning has raised significant interest, the full prediction power of network science has not yet been systematically explored in this context. Here we fill this gap by providing an in-depth statistical, combinatorial, geometrical, and topological characterization of 2D Ising snapshot networks (IsingNets) extracted from Monte Carlo simulations of the 2D Ising model at different temperatures, going across the phase transition. Our analysis reveals the complex organization properties of IsingNets in both the ferromagnetic and paramagnetic phases and demonstrates the significant deviations of the IsingNets with respect to randomized null models. In particular percolation properties of the IsingNets reflect the existence of the symmetry between configurations with opposite magnetization below the critical temperature and the very compact nature of the two emerging giant clusters revealed by our persistent homology analysis of the IsingNets. Moreover, the IsingNets display a very broad degree distribution and significant degree-degree correlations and weight-degree correlations demonstrating that they encode relevant information present in the configuration space of the 2D Ising model. The geometrical organization of the critical IsingNets is reflected in their spectral properties deviating from the one of the null model. This work reveals the important insights that network science can bring to the characterization of phases of matter. The set of tools described hereby can be applied as well to numerical and experimental data.

The Role of Knowledge in Creative Thinking

ABSTRACT In this invited paper, I briefly review my past, current, and future lines of research. The associative theory of creativity argues that higher creative individuals have a richer semantic memory structure that facilitates broader associative search processes, that leads to the combination of remote concepts into novel and appropriate ideas. Based on this theory, in my research I investigate the role of knowledge – or semantic memory – in high-level cognition, focusing on creativity, associative thinking, and memory search, in typical and clinical populations. To do so, I apply computational tools from network science, natural language processing, and machine learning, coupled with empirical cognitive and neural research. Such computational tools are enabling the representation and operationalization of the structure of semantic memory and the processes that operate over it. This is critical as it allows us to start quantifying issues that for a very long time were studied very subjectively in creativity research – remoteness of ideas, associative thinking, flexible/richer semantic memory structure, etc. Such work is offering unique, quantitative, ways to directly study classic theories of creativity, propelling forward our understanding of its complexity.

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. The source code is availiable at https://github.com/bx20000827/DL-MHNMF.git.

TRACES OF UNEQUAL ENTRY REQUIREMENT FOR ILLUSTRIOUS PEOPLE ON WIKIPEDIA BASED ON THEIR GENDER

Wikipedia is a widely used tool people use to gather knowledge about the world, causing it to have a vast impact on the way individuals perceive the reality they live in. It is then of paramount importance that the picture of the world Wikipedia provides is accurate. We cannot afford such an important tool to eschew inclusiveness or a fair representation of reality: an inaccurate picture of the world in such a tool can be used to claim unjust and unfair positions — such as that women are inferior to men — as if they were facts, because they are enshrined on an encyclopedia. In this paper, we study issues of fair gender representations for people in history noted by multiple language editions of Wikipedia: are women underrepresented on Wikipedia? We do so via a combination of natural language processing and network science. Our results indicate that there is indeed a higher bar for women to have their own biographical page on Wikipedia: women are only included when they have more significant connections than men to the rest of the network. There are visible effects of the initiatives Wikipedia is taking to fix this issue, showing that the gap is narrowing, which validates our interpretation of the data.