Small-world Networks Research Articles

Latest advancements and prospects in the next-generation of Internet of Things technologies

The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology. The traditional technologies are not enough to provide support. It is evident from the literature that complex networks are used to study the topology and the structure of a network and are applied to modern technologies. Thus, the capability of powerful computational tools and the existence of theoretical frameworks enable complex networks to derive new approaches in analyzing IoT-based technologies in terms of improving efficiency, architecture, complexity, and topology. In this direction, limited research has been carried out. The integration aspect remains a key challenge. Therefore, in order to fill this gap. Herein, we design a comprehensive literature review. In this research effort, we explore a newly leading emerging technology named the Social Internet of Things (SIoT). It is developed to overcome the challenges in IoT. We discuss the importance and the key applications of SIoT. We first presented a conceptual view along with a recent technological roadmap. The big data play an important role in the modern world. We discuss big data and the 5 Vs along with suitable applications and examples. Then, we highlighted the key concepts in complex networks, scale-free, random networks, and small-world networks. We explored and presented various graph models and metrics aligned with social networks and the most recent trends. The novelty of this research is to propose a synergy of complex networks to the IoT, SIoT, and big data together. We discuss the advantages of integration in detail. We present a detailed discussion on complex networks emerging technologies and cyber-physical systems (CPS). Briefly, our literature review covers the most recent advancements and developments in 10 years. In addition, our critical analysis is based on up-to-date surveys and case studies. Finally, we outline the impact of recent emerging technologies on challenges applications, and solutions for the future. This paper provides a good reference for researchers and readers in the IoT domain.

Center-based childcare during infancy: The relations with functional brain networks and self-regulation in young children

Given the substantial increase in children attending center-based childcare over the past decades, the consequences of center-based childcare for children’s development have gained more attention in developmental research. However, the relation between center-based childcare and children’s neurocognitive development remains relatively underexplored. The aim of this study was therefore to examine the relations between quantity of center-based childcare during infancy and the neurocognitive development (both functional brain networks and self-regulation) of 584 Dutch children. Small-world brain networks and children’s self-regulation were assessed during infancy (around 10 months of age) and the preschool period (2–6 years of age). The findings revealed that the quantity of center-based childcare during infancy was unrelated to individual differences in children’s functional brain networks. However, spending more hours per week in center-based childcare was positively related to the development of self-regulation in preschool age children, regardless of children’s sex or the levels of exposure to risk and maternal support in the home environment. More insight into the positive effects of center-based childcare on children’s development from infancy to toddlerhood can help to increase our insight into a better work–life balance and labor force participation of parents with young children. Moreover, this study highlights that Dutch center-based childcare offers opportunities to invest in positive child outcomes in children, including self-regulation.

Queer Voices in the Digital Barricades: Exploring Social Media Use in Information Resistance

This paper interrogates how queer people use the Facebook group Queer Exchange to build community and meet information needs. Research indicates that queer people face social exclusion in mainstream society, which leads to health injustices. Social media can then become a space to build inclusion, necessitating the study of queer virtual space. The study consists of 12 semi-structured interviews with queer adults living in rural communities in Western Massachusetts. The themes identified include community-sourced information, community building by identity, and reproducing offline oppressions. The paper uses de Certeau’s theory of tactics and Chatman’s small world theory to frame the research. The study concludes that Queer Exchange plays an essential role in the digital information worlds of rural queer people.

Retrieval Augmented Docking Using Hierarchical Navigable Small Worlds.

Make-on-demand chemical libraries have drastically increased the reach of molecular docking, with the enumerated ready-to-dock ZINC-22 library approaching 6.4 billion molecules (July 2024). While ever-growing libraries result in better-scoring molecules, the computational resources required to dock all of ZINC-22 make this endeavor infeasible for most. Here, we organize and traverse chemical space with hierarchical navigable small-world graphs, a method we term retrieval augmented docking (RAD). RAD recovers most virtual actives, despite docking only a fraction of the library. Furthermore, RAD is protein-agnostic, supporting additional docking campaigns without additional computational overhead. In depth, we assess RAD on published large-scale docking campaigns against D4 and AmpC spanning 99.5 million and 138 million molecules, respectively. RAD recovers 95% of DOCK virtual actives for both targets after evaluating only 10% of the libraries. In breadth, RAD shows widespread applicability against 43 DUDE-Z proteins, evaluating 50.3 million associations. On average, RAD recovers 87% of virtual actives while docking 10% of the library without sacrificing chemical diversity.

Investigating the role of structural connectivity in the individuals with moderate hearing loss

Hearing loss has been identified as a risk factor for cognitive decline. The processing of auditory information relies on a well-connected neuronal network. Previous studies have found white matter abnormalities in individuals with hearing loss, but the structural connectivity and microstructural properties of white matter in individuals with moderate hearing loss remain unclear.To examine the integrity of white matter, identify vulnerable structural connectivity, and assess network topology, we examined major white matter tracts in elderly individuals with moderate hearing loss (MHL) (>30 dB) and compared them to age-matched controls with good hearing (GH).We observed that the fractional anisotropy of the right corticospinal tract and left superior longitudinal fasciculus – parietal was higher in subjects with MHL. Additionally, we identified a disrupted network centered on the left putamen in MHL, involving eight brain regions. Network topology analysis showed reduced betweenness centrality and small-world network in MHL. Interestingly, the fractional anisotropy of the forceps major tract and left uncinate tracts were correlated with hearing status.Our findings suggest that MHL is associated with putamen-centered disruptions in structural connectivity. The increased fractional anisotropy of the right corticospinal tract may be a compensatory mechanism, as it projects fibers to the right putamen. Overall, these results provide valuable insights into the impact of hearing loss on white matter integrity and may inform the development of new treatments to prevent its progression.

Characterization of Industrial Carbon Emission Dynamics and Network Structure Evolution in China

Given that industry is the major source of carbon emissions, clarifying the carbon transfer regularity triggered by industry production linkage is of great importance in realizing the synergistic development of industry to reduce pollution and carbon emissions. Based on the perspective of responsibility allocation, the study accounted for the industry carbon emissions and constructed a carbon transfer weighted directed network model and applied the social network analysis method to explore the structure of the industry carbon transfer network and its evolution characteristics from 2005 to 2020. The results showed that： ① The overall carbon emissions of industries from 2005 to 2020 presented a rapid growth trend and large differences occurred in emissions between industries. ② The scale of the carbon transfer network was expanding rapidly and the characteristics of the small-world network were significant. ③ The community structure of the carbon transfer network was obvious, including four optimal associations： high carbon emission, carbon transmission, carbon endogenous, and high spillover. ④ The construction industry and the metal smelting and rolling processing industry were the core of the carbon transfer network structure, with strong network control. In view of the above characteristics, we proposed countermeasures and suggestions in terms of implementing the allocation of carbon emission reduction responsibilities and differentiated emission reduction measures for associations as well as focusing on the management of core nodes of the network, with the intent of providing cross-sectoral synergistic emission reduction ideas for the development of sustainable low-carbon transformation in China's industry sector.

DFC-Igloo: A Dynamic Functional Connectome Learning Framework for Identifying Neurodevelopmental Biomarkers in Very Preterm Infants

Background and ObjectiveVery preterm infants are susceptible to neurodevelopmental impairments, necessitating early detection of prognostic biomarkers for timely intervention. The study aims to explore possible functional biomarkers for very preterm infants at born that relate to their future cognitive and motor development using resting-state fMRI. Prior studies are limited by the sample size and suffer from efficient functional connectome (FC) construction algorithms that can handle the noisy data contained in neonatal time series, leading to equivocal findings. Therefore, we first propose an enhanced functional connectome construction algorithm as a prerequisite step. We then apply the new FC construction algorithm to our large prospective very preterm cohort to explore multi-level neurodevelopmental biomarkers. MethodsThere exists an intrinsic relationship between the structural connectome (SC) and FC, with a notable coupling between the two. This observation implies a putative property of graph signal smoothness on the SC as well. Yet, this property has not been fully exploited for constructing intrinsic dFC. In this study, we proposed an advanced dynamic FC (dFC) learning model, dFC-Igloo, which leveraged SC information to iteratively refine dFC estimations by applying graph signal smoothness to both FC and SC. The model was evaluated on artificial small-world graphs and simulated graph signals. ResultsThe proposed model achieved the best and most robust recovery of the ground truth graph across different noise levels and simulated SC pairs from the simulation. The model was further applied to a cohort of very preterm infants from five Neonatal Intensive Care Units, where an enhanced dFC was obtained for each infant. Based on the improved dFC, we identified neurodevelopmental biomarkers for neonates across connectome-wide, regional, and subnetwork scales. ConclusionThe identified markers correlate with cognitive and motor developmental outcomes, offering insights into early brain development and potential neurodevelopmental challenges.

Heterogeneous emotional contagion of the cyber–physical society

When emergencies occur, panic spreads quickly across cyberspace and physical space. Despite widespread attention to emotional contagion in cyber–physical societies (CPS), existing studies often overlook individual relationship heterogeneity, which results in imprecise models. To address this issue, we propose a heterogeneous emotional contagion method for CPS. First, we introduce the Strong–Weak Emotional Contagion Model (SW-ECM) to simulate the heterogeneous emotional contagion process in CPS. Second, we formulate the mean-field equations for the SW-ECM to accurately capture the dynamic evolution of heterogeneous emotional contagion in the CPS. Finally, we construct a small-world network based on strong–weak relationships to validate the effectiveness of our method. The experimental results show that our method can effectively simulate the heterogeneous emotional contagion and capture changes in relationships between individuals, providing valuable guidance for crowd evacuations prone to emotional contagion.

Research on coal mine safety risk evolution and key hidden dangers under the perspective of complex network

In order to find out the main causes of coal mine safety accidents and improve the pertinence of coal mine safety risk management and control, the identification and analysis of coal mine safety risks and hidden dangers are carried out based on the analysis of coal mine accident reports. Combing the complex network theory, a complex network model for the evolution of coal mine safety risks is constructed. The key elements that affect coal mine safety risk accidents are obtained through quantitative research on the characteristic indicators of the complex network model of coal mine safety risks. And the key nodes of coal mine safety risk spread network are obtained through network interference to the overall efficiency. The research results show that the complex network of coal mine safety risks illustrate the characteristics of a small-world network, and the spread of a certain risk is likely to cause coal mine safety accidents. Strengthening the risk management and control of hidden dangers with higher intermediate centrality can isolate the spread of coal mine safety risks and reduce the possibility of coal mine accidents.

Abnormal suppression of thermal transport by long-range interactions in networks

Heat and electricity are two fundamental forms of energy widely utilized in our daily lives. Recently, in the study of complex networks, there is growing evidence that they behave significantly different at the micro-nanoscale. Here, we use a small-world network model to investigate the effects of reconnection probability p and decay exponent α on thermal and electrical transport within the network. Our results demonstrate that the electrical transport efficiency increases by nearly one order of magnitude, while the thermal transport efficiency falls off a cliff by three to four orders of magnitude, breaking the traditional rule that shortcuts enhance energy transport in small-world networks. Furthermore, we elucidate that phonon localization is a crucial factor in the weakening of thermal transport efficiency in small-world networks by characterizing the density of states, phonon participation ratio, and nearest-neighbor spacing distribution. These insights will pave new ways for designing thermoelectric materials with high electrical conductance and low thermal conductance.

Energy criterion for synchronization of neuron populations

Energy regulates the neuron activities, including inter-neuronal communication, biorhythm, and brain function. In this paper, we propose the energy criterion for the synchronization of neuron populations, and neurons tend to be synchronous if the energy variance approaches zero. Furthermore, we investigate the energy distribution of neuron population. The individual energy groups, two or three groups, expand until they merge into a uniform energy group with the increasing of the coupling intensity. Then, the uniform energy group tends to be a Normal distribution when the neuron population is synchronized. The energy criterion and energy distribution for synchronization are confirmed in the chain network, small world network and the scale free network composed by the FithzHugh-Nagumo (FHN) neurons. These results advance the understanding of the role played by the energy in neuron populations.

Determinants of successful disease control through voluntary quarantine dynamics on social networks

In the wake of epidemics, quarantine measures are typically recommended by health authorities or governments to help control the spread of the disease. Compared with mandatory quarantine, voluntary quarantine offers individuals the liberty to decide whether to isolate themselves in case of infection exposure, driven by their personal assessment of the trade-off between economic loss and health risks as well as their own sense of social responsibility and concern for public health. To better understand self-motivated health behavior choices under these factors, here we incorporate voluntary quarantine into an endemic disease model – the susceptible–infected–susceptible (SIS) model – and perform comprehensive agent-based simulations to characterize the resulting behavior-disease interactions in structured populations. We quantify the conditions under which voluntary quarantine will be an effective intervention measure to mitigate disease burden. Furthermore, we demonstrate how individual decision-making factors, including the level of temptation to refrain from quarantine and the degree of social compassion, impact compliance levels of voluntary quarantines and the consequent collective disease mitigation efforts. We find that successful disease control requires either a sufficiently low level of temptation or a sufficiently high degree of social compassion, such that even complete containment of the epidemic is attainable. In addition to well-mixed populations, we have also analyzed other more realistic social networks of contacts, including spatial lattices, small-world networks, and real social networks. Our work offers new insights into the fundamental social dilemma aspect of disease control through non-pharmaceutical interventions, such as voluntary quarantine and isolation, where the collective outcome of individual decision-making is crucial.

Investigating the interaction between EEG and fNIRS: A multimodal network analysis of brain connectivity

The brain is a complex system with functional and structural networks. Different neuroimaging methods have been developed to explore these networks, but each method has its own unique strengths and limitations, depending on the signals they measure. Combining techniques like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) has gained interest, but understanding how the information derived from these modalities is related to each other remains an exciting open question. The multilayer network model has emerged as a promising approach to integrate different sources data. In this study, we investigated the hemodynamic and electrophysiological data captured by fNIRS and EEG to compare brain network topologies derived from each modality, examining how these topologies vary between resting state (RS) and task-related conditions. Additionally, we adopted the multilayer network model to integrate EEG and fNIRS data and evaluate the benefits of combining multiple modalities compared to using a single modality in capturing characteristic brain functioning.A small-world network structure was observed in the rest, right motor imagery, and left motor imagery tasks in both modalities. We found that EEG captures faster changes in neural activity, thus providing a more precise estimation of the timing of information transfer between brain regions in RS. fNIRS provides insights into the slower hemodynamic responses associated with longer-lasting and sustained neural processes in cognitive tasks. The multilayer approach outperformed unimodal analyses, offering a richer understanding of brain function. Complementarity between EEG and fNIRS was observed, particularly during tasks, as well as a certain level of redundancy and complementarity between the multimodal and the unimodal approach, which depends on the modality and the specific brain state. Overall, the results highlight differences in how EEG and fNIRS capture brain network topology in RS and tasks and emphasize the value of integrating multiple modalities for a comprehensive view of brain connectivity and function.

Synchronizability of multi-layer small-world dynamical networks

This paper investigates the synchronizability of multi-layer small-world dynamical networks and discusses the significant factors affecting the synchronizability. First, we introduce the network model of multi-layer small-world dynamical networks with one-to-one connections in the inter-layer connections and each layer has the same topology. Second, we consider the topological parameters including adding probability, intra-layer coupling strength, inter-layer coupling strength, number of nodes per layer, initial node degree, and number of network layers which will influence the network’s synchronizability. Third, we adopt the master stability function method and numerical simulations to analyze the synchronizability of the network. We also examine how the topological parameters influence the synchronizability of multi-layer small-world dynamical networks and the relationships among these parameters. Finally, synchronization control experiments are conducted to verify our results. As a result, we find that only increasing the number of nodes per layer will weaken the synchronizability of multi-layer small-world dynamical networks, while increasing other topological parameters will enhance the synchronizability. The current findings enable us to gain a deeper understanding of the synchronization behavior and characteristics of multi-layer small-world dynamical networks.

Fractal version of average Fermat distance on some small-world hierarchical networks

Fermat–Wiener index based on topological Wiener index is the total sum of Fermat distance over all the triplets for vertices. In this paper, we construct a class of hierarchical graphs based on hierarchical product generalized from Cartesian product. We study some critical properties of the hierarchical networks by investigating its topological indices. Applying the finite pattern method, we analytically deduce the dominant term of average Fermat distance and obtain its asymptotic formula, which implies small-world property. We finally exhibit a close connection between Fermat–Wiener index and related graph invariants like average geodesic distance, Wiener index and eigenvalues of Laplacian matrix.

Assemblage Robotic Plants: Individualizations of Many Orders of Magnitude

Abstract This paper focuses on the author’s artistic practice and presents robotic plant creations as assemblages using robotic autonomy unities in a Small-World Network configuration. This artistic production is significantly founded on Gilbert Simondon’s philosophical concepts about Technological Beings. Understanding these communities of robotic structures as individualizations is critical to apprehending them as individuals. The paper also discusses the implications of hardware design, coding, and the concept of artificial life related to the development of such technological organisms.

Abnormal Topological Organization of Human Brain Connectome in Primary Dysmenorrhea Patients Using Graph Theoretical Analysis.

Accumulating studies have revealed altered brain function and structure in regions linked to sensory, pain and emotion in individuals with primary dysmenorrhea (PD). However, the changes in the topological properties of the brain's functional connectome in patients with PD experiencing chronic pain remain poorly understood. Our study aimed to explore the mechanism of functional brain network impairment in individuals withPD through a graph-theoretic analysis. This study was a randomized controlled trial that included individuals with PD and healthy controls (HC) from June 2021 to June 2022. The experiment took place in the magnetic resonance imaging facility at Jiangxi Provincial People's Hospital. Static MRI scans were conducted on 23 female patients with PD and 23 healthy female controls. A two-sample t-test was conducted to compare the global and nodal indices between the two groups, while the Network-Based Statistics (NBS) method was utilized to explore the functional connectivity alterations between the groups. In the global index, The PD group exhibited decreased Sigma (p = 0.0432) and Gamma (p = 0.0470) compared to the HC group among the small-world network properties.(p<0.05) In the nodal index, the PD group displayed reduced betweenness centrality and increased degree centrality in the default mode network (DMN), along with decreased nodal efficiency and increased degree centrality in the visual network (VN). (P < 0.05, Bonferroni-corrected) Furthermore, in the connection analysis, PD patients showed altered functional connectivity in the basal ganglia network (BGN), VN, and DMN.(NBS corrected). Our results indicate that individuals with PD showed abnormal brain network efficiency and abnormal connection within DMN, VN and BGN related to pain matrix. These findings have important references for understanding the neural mechanism of pain in PD.

Reputation-based disconnection-reconnection mechanism in Prisoner's Dilemma Game within dynamic networks

Individual reputation significantly influences players’ decision-making. Players tend to disconnect from neighbors with lower reputations so as to mitigate potential losses in the game. Therefore, it is crucial and meaningful to explore how to apply reputation in the game to enhance cooperation. In this study, we propose a reputation-based evolution mechanism in the Prisoner's Dilemma Game (PDG) within dynamic complex networks, including square lattices and small-world networks. We introduce the reputation factor α and the selection intensity σ in order to measure the changes in reputation and their impact objectively. Moreover, considering the impact of information connectivity, we define the environmental degree r and investigate the effects of environmental degree r and rewiring probability p on cooperation rates. Simulation results demonstrate that the reputation factor and information connectivity have a positive promoting effect on cooperative behavior; conversely, the increase in selection intensity affects cooperation negatively.

Exploration of a brain-inspired photon reservoir computing network based on quantum-dot spin-VCSELs

Based on small-world network theory, we have developed a brain-inspired photonic reservoir computing (RC) network system utilizing quantum dot spin-vertical-cavity surface-emitting lasers (QD spin-VCSELs) and formulated a comprehensive theoretical model for it. This innovative network system comprises input layers, a reservoir network layer, and output layers. The reservoir network layer features four distinct reservoir modules that are asymmetrically coupled. Each module is represented by a QD spin-VCSEL, characterized by optical feedback and optical injection. Within these modules, four chaotic polarization components, emitted from both the ground and excited states of the QD Spin-VCSEL, form four distinct reservoirs through a process of asymmetric coupling. Moreover, these components, when emitted by the ground and excited states of a driving QD spin-VCSEL within a specific parameter space, act as targets for prediction. Delving further, we investigated the correlation between various system parameters, such as the sampling period, the interval between virtual nodes, the strengths of optical injection and feedback, frequency detuning, and the predictive accuracy of each module’s four photonic RCs concerning the four designated predictive targets. We also examined how these parameters influence the memory storage capabilities of the four photonics RCs within each module. Our findings indicate that when a module receives coupling injections from more than two other modules, and an RC within this module is also subject to coupling injections from over two other RCs, the system displays reduced predictive errors and enhanced memory storage capacities when the system parameters are fixed. Namely, the superior performance of the reservoir module in predictive accuracy and memory capacities follows from its complex interaction with multiple light injections and coupling injections, with its three various PCs benefiting from three, two, and one coupling injections respectively. Conversely, variations in optical injection and feedback strength, as well as frequency detuning, introduce only marginal fluctuations in the predictive errors across the four photonics RCs within each module and exert minimal impact on the memory storage capacity of individual photonics RCs within the modules. Our investigated results contribute to the development of photonic reservoir computing towards fast response biological neural networks.

Brain Responses Difference between Sexes for Strong Desire to Void: A Functional Magnetic Resonance Imaging Study in Adults Based on Graph Theory.

Background: The alternations of brain responses to a strong desire to void were unclear, and the gender differences under the strong desire to void remain controversial. The present study aims to identify the functional brain network's topologic property changes evoked by a strong desire to void in healthy male and female adults with synchronous urodynamics using a graph theory analysis. Methods: The bladders of eleven healthy males and eleven females were filled via a catheter using a specific infusion and withdrawal pattern. A resting-state functional magnetic resonance imaging (fMRI) was performed on the enrolled subjects, scanning under both the empty bladder and strong desire to void states. An automated anatomical labeling (AAL) atlas was used to identify the ninety cortical and subcortical regions. Pearson's correlation calculations were performed to establish a brain connection matrix. A paired t-test (p < 0.05) and Bonferroni correction were applied to identify the significant statistical differences in topological properties between the two states, including small-world network property parameters [gamma (γ) and lambda (λ)], characteristic path length (Lp), clustering coefficient (Cp), global efficiency (Eglob), local efficiency (Eloc), and regional nodal efficiency (Enodal). Results: The final data suggested that females and males had different brain response patterns to a strong desire to void, compared with an empty bladder state. Conclusions: More brain regions involving emotion, cognition, and social work were active in females, and males might obtain a better urinary continence via a compensatory mechanism.