Network Structure Research Articles

The Semantic Web has transformed the way data is represented, shared, and integrated across multiple domains. However, as its size and interconnectedness continue to grow, it becomes increasingly exposed to cyber threats. Securing the Semantic Web is therefore a critical challenge, as traditional security methods often fail to protect the highly interlinked data, ontologies, and network structures on which it relies. This paper proposes a novel, nature-inspired cybersecurity approach that uses swarm optimization algorithm to improve the resilience of Semantic Web. These algorithms, modeled on the collective behavior of insects, can efficiently allocate limited security resources to detect and mitigate potential threats in real time. By applying a distributed and adaptive defense mechanism based on swarm optimization, Semantic Web nodes can autonomously respond to evolving attack patterns, reducing vulnerabilities and strengthening overall system security. The results demonstrate a significant improvement in the network's robustness against various attack scenarios, including those targeting ontologies and data relationships. The proposed nature-inspired strategy enables secure and reliable information exchange across distributed systems while adapting dynamically to new cyber threats.

Predicting drug-target interactions (DTIs) plays a pivotal role in accelerating drug repositioning by prioritizing candidate drugs and reducing experimental costs. Despite advancements in deep learning, several challenges still require further exploration, including sparsity and inadequate representation of feature relationships. We propose GCNMM, a novel graph convolutional network based on meta-paths and mutual information, to predict latent DTIs in drug-target heterogeneous networks. Our approach begins by constructing a fused DTI network based on meta-paths and a graph attention network. We compute multiple similarity networks by using Jaccard coefficients and integrate them into the fused drug and target similarity networks through entropy-based fusion. These networks are then jointly processed by graph convolutional auto-encoder to generate low-dimensional feature representations. To preserve the topological structure of the original network in the embedding space and strengthen the relationship between the input and latent representations, we incorporate spatial topological consistency and mutual information maximization as dual optimization objectives. The experimental results illustrate that GCNMM exhibits superior performance to existing baseline models in DTI prediction. Furthermore, case studies validate the practical effectiveness of GCNMM, highlighting its potential in DTI prediction and drug repositioning.

Recent efforts of nation states to enhance resilience and restructure global industry, supply chains, and value chains have intensified the dual economic structure that shapes flows at both domestic and international levels. Multi-regional input-output (MRIO) tables provide a quantitative approach to capture inter-regional and inter-sectoral economic flows. Existing MRIO databases mainly consist of international MRIO and subnational MRIO, respectively. However, they lack coupling MRIO tables and thus fail to adequately represent the dual structure of subnational and international trade. In this study, we develop a subnational and international coupling (SIC) MRIO in 2017, covering 30 sectors, 30 provinces in China and 66 countries & economies. We employ macro-level aggregated data, micro-level structural data and large language models (LLMs) to construct the SIC method. Our SIC MRIO captures the interplay between international and subnational economic flows, reflecting the emerging dual structure of production and consumption networks in the global landscape. It can serve as a baseline for future studies of the economic and environmental implications of chains under the dual structure.

Mammalian cells sense and respond to environmental changes using a complex and intelligent system that integrates chemical and mechanical signals. The transduction of mechanical cues into chemical changes modulates cell physiology, allowing a cell to adapt to its microenvironment. Understanding how the chemical and mechanical regulatory modules interact is crucial for elucidating mechanisms of mechanosensation and cellular homeostasis. In this study, we find that cells exhibit nonmonotonic changes in cell volume and intracellular pH when subjected to physical stimuli and varying degrees of actomyosin cytoskeleton disruption. We find that these nonmonotonic responses are mediated by a chemical compensation mechanism, where the attenuation of actomyosin activity stimulates the activity of PI3K/Akt pathway. This, in turn, activates sodium-hydrogen exchanger 1 (NHE1), resulting in elevated intracellular pH and increased cell volume. Furthermore, we identify a competitive interaction between the PI3K/Akt and MAPK/ERK pathways-two major regulators of cell proliferation and motility. This competition modulates the chemical compensation based on the relative activities of these pathways. Our mathematical modeling reveals the network structure that is essential for establishing the nonmonotonic response. Interestingly, this regulatory system is altered in HT1080 fibrosarcoma, highlighting a potential mechanistic divergence in cancer cells in contrast to their normal-like counterpart, such as NIH 3T3 and HFF-1 fibroblasts. Overall, our work reveals a compensatory mechanism between chemical and mechanical signals, providing an infrastructure to elucidate the integrated mechanochemical response to environmental stimuli.

Word retrieval difficulties, such as anomia, increase with age. While some language functions, like semantic knowledge, remain stable or improve, lexical retrieval declines, due to reduced processing speed, weaker inhibition, and increased lexical competition. The Picture-Word Interference (PWI) paradigm is commonly used to study these effects. Taxonomic relations (e.g. cow - horse) tend to increase interference, whereas thematic relations (e.g. cow - milk) may facilitate retrieval. This study investigates how semantic links influence lexical retrieval and whether these effects vary with age. Fifty-five French-speaking adults (30 young, 25 elderly) performed a PWI task (i.e. a picture is shown with a written distractor word) with four conditions: taxonomic, thematic, unrelated, and neutral. Elderly adults responded more slowly and less accurately overall. However, taxonomic distractors consistently caused the greatest interference eliciting the longest reaction times and lowest accuracy - in both age groups. Thematic and unrelated distractors produced moderate interference compared to the neutral condition. Semantic relationships distinctly influence lexical retrieval, regardless of age. Age-related differences mainly manifested in speed and accuracy while the structure of semantic interference is overall consistent across ages, underscoring, respectively, cognitive slowing and reduced inhibitory control in aging. Preserved semantic knowledge in elderly adults appears to mitigate some of these difficulties, highlighting its compensatory role in language production. The findings highlight the specific role of semantic relations in lexical access and confirm that aging affects processing speed more than semantic network structure.

Purpose This study aim to propose a conceptual model to explain and enhance knowledge sharing in small and medium-sized enterprises (SMEs), with a particular focus on the role of social capital, network structure and social networking technologies (SNTs) in facilitating tacit knowledge flows across supply chains. Design/methodology/approach The study integrates theoretical foundations from social capital theory, social network analysis (SNA) and the knowledge-based view (KBV) of the firm to build a multidimensional conceptual framework. The model is illustrated through a case-based application in a culturally embedded SME ecosystem in Alcoi (Spain), using qualitative methods, including document analysis, semi-structured interviews and participant observation. Findings The model identifies how structural and relational social capital, network configuration and SNTs jointly enable knowledge sharing processes, especially tacit knowledge. The case demonstrates that SMEs embedded in dense, trust-based networks can leverage simple digital tools and educational partnerships to enhance collaboration, innovation and supply chain agility. Research limitations/implications This is a conceptual and exploratory contribution. Future research should empirically validate the model across multiple SME settings and develop instruments to measure knowledge-sharing readiness and digital maturity. Practical implications The model provides actionable insights for SME managers and policymakers. It supports the design of strategies to reinforce trust-based knowledge flows, strengthen network ties and adopt digital tools aligned with informal collaboration practices. Social implications Knowledge sharing is shown to contribute to community resilience, intergenerational learning and the preservation of cultural identity in regional production ecosystems. The model also supports inclusive innovation policies for SMEs. Originality/value This paper offers a novel integration of KM and supply chain perspectives by combining social capital and SNA with digital enablers of tacit knowledge exchange. It contributes both a conceptual framework and a practical roadmap for enhancing knowledge flows in SME-dominated environments.

Urban heat is a growing concern especially under global climate change and continuous urbanization. However, the understanding of its spatiotemporal propagation behaviours remains limited. In this study, we leverage a data-driven modelling framework that integrates causal inference, network topology analysis and dynamic synchronization to investigate the structure and evolution of temperature-based causal networks across the continental United States. We perform the first systematic comparison of causal networks constructed using warm-season daytime and nighttime air temperature anomalies in urban and surrounding rural areas. Results suggest strong spatial coherence of network links, especially during nighttime, and small-world properties across all cases. In addition, urban heat dynamics becomes increasingly synchronized across cities over time, particularly for maximum air temperature. Different network centrality measures consistently identify the Great Lakes region as a key mediator for spreading and mediating heat perturbations. This system-level analysis provides new insights into the spatial organization and dynamic behaviours of urban heat in a changing climate.This article is part of the theme issue 'Urban heat spreading above and below ground'.

SUMMARYBacteria are frequently subject to potentially lethal temperature shifts in their natural environments. We review the changes in the structure and dynamics of the gene regulatory network of the bacterium Escherichia coli during cold shocks. First, we describe the effects of cold shocks on higher-order cellular structures (cytoplasm and membrane) and functions (growth, division, and biofilm formation). Next, we focus on the nucleoid, DNA supercoiling, topoisomerases, ATP, and nucleoid-associated proteins. Afterward, we describe the mutual effects of changes in transcription dynamics and DNA supercoiling during cold shocks, followed by the consequent genome-wide, time-lapse changes in the transcriptome. Finally, we briefly describe the post-transcriptional effects of cold shocks and the cellular processes of acclimatization. In the end, we discuss how studying this topic can assist in developing temperature-sensitive synthetic genetic circuits, efficient bioindustrial processes, and new means to cope with bacterial antibiotic tolerance.

Dexmedetomidine produces more sleep-like brain activity compared with propofol in human participants.

Abstract Social networks formed via play interactions offer a powerful framework for investigating the social dynamics of animals and the functions of play behavior. Using this framework in mantled howler monkeys ( Alouatta palliata ), we examined whether social play might fulfil some of the affiliative and bonding functions typically addressed to allogrooming. We analyzed play networks in seven groups of mantled howler monkeys (89 individuals), analyzing group and individual-level patterns based on 1774 observation hours collected over approximately 3 years. We examined the relationship between group size and network complexity using edge density (the proportion of connections), modularity (the degree to which the network is subdivided into clusters), and average path lengths (the number of steps required to reach others in the network). We analyzed centrality across age-sex classes using betweenness, closeness, and eigenvector metrics. We tested which centrality measure best predicted play evenness, defined as the degree to which individuals distributed their playtime across partners. We found that network interconnectivity generally declined with group size, though average path lengths were not significantly different from those of random networks. Immatures showed the highest centrality values, while adult females exhibited the highest play evenness. However, centrality measures did not significantly predict play evenness. These patterns suggest age-specific functions of social play: immatures may benefit from frequent play to support development, while adult females may use evenly distributed play to navigate social competition. Our findings support the hypothesis that social play serves similar functions to allogrooming, contributing to social bonding.

Web phishing, a significant form of cybercrime, seeks to obtain illegal access to users’ sensitive information. Despite the abundance of web phishing detection datasets, a systematic framework for their comparative analytical evaluation is lacking. This paper addresses this gap by proposing a novel graph-based framework for analyzing Web phishing detection datasets through network science. We construct Features’ Similarity Graphs (FSGs) for six major datasets – Tamal, Tan, PhiUSIIL, Hannousse, Vrbančič, and Kumar – using three similarity measures: Pearson Correlation Coefficient, Mutual Information, and Kendall’s Tau, which captures linear, non-linear, and rank-order feature interdependencies, respectively. From each FSG and its giant component, we extract 15 key metrics to characterize network structure and feature relationships. Our analysis reveals distinct dataset characteristics, guiding researchers in selecting appropriate datasets for specific research objectives. For instance, Tamal and Vrbančič are ideal for graph-based approaches due to their high graph density, while Tan excels in visual and spatial similarity-based methods. PhiUSIIL is suited for hybrid approaches, Hannousse for deep learning models, and Kumar for lightweight, resource-efficient applications. This framework provides a systematic comparison of Web phishing datasets and offers practical recommendations for the research community, enabling more adaptive phishing detection systems.

Agriculture is a driving factor of climate change and biodiversity loss. Conventional agricultural practices, including the conversion of diverse habitats into monocultures, threaten wild and managed pollinators that provide the essential service of pollination. Polyculture production, the simultaneous cultivation of more than one crop, is a widely proposed alternative system with benefits for sustainable food production and biodiversity conservation. This study examines seasonal plant-pollinator interaction networks in the UF/IFAS Horticultural Sciences Department Teaching Garden to assess the ability of a small-scale agricultural system to support non-managed pollinators throughout the year. Interactions between honey bees, non-honey bees, the plants they visit, and overall bee diversity were evaluated. Seasonal variation in plant blooms, specifically blueberry and sunflower, suggest a dynamic shift in network structure and function between the two seasons. Using evidence from the garden, this study suggests management strategies to support North American native pollinators in agricultural systems.

Abstract The limited operational stability of organic solar cells (OSCs) remains a major barrier to their practical application. In this work, two highly crystalline 2D acceptors, PhIC‐BO and AnIC‐BO are reported, which are incorporated as a third component into bilayer‐dominated quasiplanar heterojunction (Q‐PHJ) OSCs to simultaneously enhance efficiency and stability. PhIC‐BO, featuring a phenanthrene extension, forms a typical 3D network crystal with an elliptical framework, while the anthracene‐based AnIC‐BO adopts a linear packing motif, resulting in a quasi‐3D network structure. Owing to their high compatibility with host materials, both acceptors preferentially accumulate within the narrow bulk heterojunction (BHJ) region of the Q‐PHJ architecture, facilitating charge generation and simultaneously acting as a diffusion barrier to suppress molecular intermixing. These synergistic effects lead to significant improvements in both device performance and long‐term stability. The resulting PhIC‐BO‐based ternary OSCs exhibit a high power conversion efficiency (PCE) of 19.44% and retain 99% of their initial PCE after 6245 h of storage (with an extrapolated T 80 lifetime of ≈58 600 h), ranking among the most stable high‐performance OSCs reported to date. This study demonstrates a molecular design strategy that bridges high efficiency and operational stability, offering practical guidelines for the development of commercially viable OSCs.

Abstract In contrast to the neatly bounded spectra of densely populated large random matrices, sparse random matrices often exhibit unbounded eigenvalue tails on the real and imaginary axis, called Lifshitz tails. In the case of asymmetric matrices, concise mathematical results have proved elusive. In this work, we present an analytical approach to characterising these tails. We exploit the fact that eigenvalues in the tail region have corresponding eigenvectors that are exponentially localised on highly-connected hubs of the network associated to the random matrix. We approximate these eigenvectors using a series expansion in the inverse connectivity of the hub, where successive terms in the series take into account further sets of next-nearest neighbours. By considering the ensemble of such hubs, we are able to characterise the eigenvalue density and the extent of localisation in the tails of the spectrum in a general fashion. As such, we classify a number of different asymptotic behaviours in the Lifshitz tails, as well as the leading eigenvalue and the inverse participation ratio. We demonstrate how an interplay between matrix asymmetry, network structure, and the edge-weight distribution leads to the variety of observed behaviours.

To enhance the storage stability, physical rheological properties, and antiaging performance of waste rubber powder modified asphalt, this study employed long-chain silane coupling agents (C6TMS and C16TMS) for surface modification of red mud (RM), successfully preparing organic-modified red mud (C6RM/C16RM). FTIR and XRD analyses confirmed that silane chains were successfully grafted onto the RM surface without altering its crystalline structure. SEM and particle size analysis revealed significantly improved dispersion of the modified red mud, while water contact angle tests indicated marked enhancement of its hydrophobicity. When C6RM/C16RM was compounded with waste rubber powder for aged asphalt regeneration, results showed that modified red mud substantially improved the comprehensive performance of recycled asphalt. Notably, C16RM demonstrated superior effects by enhancing asphalt's storage stability, rheological properties, and antiaging performance. Mechanism analysis revealed that the long organic chains of red mud intertwined within the asphalt matrix, forming a cross-link-like network structure that effectively inhibited rubber powder sedimentation and improved system stability. Additionally, the porous structure and uniform dispersion of modified red mud in asphalt hindered oxygen/heat penetration and adsorbed light components, thereby delaying asphalt aging. This research provides an effective strategy for improving waste rubber powder modified asphalt performance while achieving resource utilization of red mud, carrying significant environmental and economic value.

In recent years, Geographical Information Systems (GIS) and their associated databases have become essential tools in the management of water supply infrastructure. Their application in water utilities extends beyond public health protection and now plays a pivotal role in network operation, maintenance planning, and risk analysis. This study focuses on the integration of GIS tools, operational data, and failure records in the risk - based management of water distribution systems, with particular attention to minimizing disruptions in water supply to consumers. A fundamental requirement for reliable operation of a water supply system is detailed knowledge of its network structure, operating conditions, technical status, and historical data on system failures. Modern GIS platforms, especially when integrated with other digital tools such as SCADA systems, hydraulic models, and monitoring software, provide a robust framework for this. One of the most valuable GIS functionalities for both water suppliers and consumers is the systematic registration of failures in the water distribution network. Failure logs, compiled over several years, offer critical insights into the causes, frequency, and seasonality of breakdowns. These dataset s serve as the foundation for assessing infrastructure reliability and planning targeted maintenance interventions. This study presents an example of failure analysis conducted on a selected water supply network in Poland. The analysis highlights dominant failure causes and their temporal distribution. Using GIS - based numerical maps and failure databases, spatial distribution and intensity of pipe damage were evaluated. This facilitated the identification of high - risk areas and pipelines with elevated failure rates, which pose the greatest threat to continuous water supply. Risk mapping based on failure frequen cy and infrastructure condition supports decision - making in the allocation of repair resources and scheduling of rehabilitation works. This approach not only improves the effectiveness of maintenance teams but also reduces the risk of service interruptions. Moreover, the methodology is aligned with broader European policies such as the INSPIRE Directive, which promotes harmonized spatial data infrastructures as a basis for environmental and risk assessments. In an era where informatization drives operational efficiency, GIS and related information systems offer unmatched potential in the risk assessment and management of water distribution infrastructure. Their ability to process and visualize complex datasets transforms raw operational data into actionable intelligence. The outcome is a proactive maintenance strategy that enhances the resilience and security of water supply systems, ultimately ensuring uninterrupted service delivery to consumers.

Against the backdrop of colonial sugar exports, US and Soviet patronage, and the austerity of the Special Period, Cuba's pursuit of food sovereignty remains a work in progress. The government's 2021 food sovereignty policy adds nuance to the term by foregrounding nutritional and cultural diversity to combat the unhealthy options served up by an emerging private sector. Food sovereignty's shifting significance through history does not undermine its efficacy; rather, it furnishes narratives that appeal to a wide range of local and foreign actors: about revolutionary struggle, entrepreneurial initiative, personal and ecological health, and the need to counterbalance growing Chinese influence. Case studies of organic farms in inner‐city and peri‐urban Havana show intermediaries historicizing food sovereignty across scales and networks to reconcile the sensibilities of neighbours, officials, and foreign visitors. I argue that how these intermediaries temporalize the term – as a static and patriotic Cuban tradition in the first case, and as a dynamic process of political reform in the second – influences the structure of international networks and corresponding understandings of its meaning. This observation helps answer the question posed by Marc Edelman et al. in 2014: ‘Who is the sovereign in food sovereignty?’. I suggest that who the sovereign is depends on why they join networks, and that intermediaries amplify and harmonize their motivations for doing so. The international appeal of Cuban food sovereignty is therefore culturally contingent, but it is not coincidental.

We introduce a new quantity known as the network heterogeneity index, denoted by , to facilitate the investigation of disease propagation and population persistence in heterogeneous environments. Our mathematical analysis reveals that this index embodies the structure of such networks, the disease or population dynamics of patches, and the dispersal between patches. We present multiple representations of the network heterogeneity index and demonstrate that . Moreover, we explore the applications of in epidemiology and ecology across various heterogeneous environments, highlighting its effectiveness in determining disease invasibility and population persistence.

Assessing the fingerprint of a material's microstructure is key for supporting materials design. With the emergence of a wide range of 3D characterization techniques, it is critical to understand the main differences in fingerprints reconstructed from 2D and 3D datasets. To this end, we introduce a graph-based microstructure reconstruction framework that enables structural comparisons of twin domain networks in high purity Ti using 3D and 2D electron backscatter diffraction. Insights into the structure of the twin networks are facilitated by combining statistical analysis of twin crystallography with visual and graphical analysis of the novel graph abstractions of the twins. We demonstrate that compared to 3D reconstructions, conventional 2D views of twinning miss key aspects of the microstructure including the high interconnectivity of domains into networks that span the full reconstruction volume. The reduced cross-grain and in-grain twin connectivity typically observed in 2D has notable implications on our understanding of how twinning mediates the plastic response of microstructures and how twin networks evolve. It is thus clear that 3D characterization is critical for accurately inferring both twin network morphologies as well as the key unit processes facilitating network formation.

Abstract This paper develops a theoretical model of cyberwarfare between nations, focusing on the factors that determine the severity and outcomes of cyber conflicts. We introduce a two‐country model where nations invest in offensive or defensive cyber capabilities across networked systems. We show that resource expenditure intensifies when players' effective values are similar, which can help explain the rise of cyberwarfare. We explore the implications of network structures, showing how larger attack surfaces worsen outcomes for defenders. Additionally, we investigate the impact of private cyber defence provision, and find that centralized policies may either improve or exacerbate cyber conflict.