Network Measures Research Articles

Coastal CUBEnet: an integrated observation and modeling system for sustainable Northern Gulf of Mexico coastal areas

The University of Southern Mississippi has developed the coastal CUBEnet environment. Coastal CUBEnet is a high-resolution, coastal ocean sensor, modeling, and data sharing web-based network that provides the environmental intelligence needed to support the complex modeling of the interlinked processes in the northern Gulf of Mexico. With near-real time data delivery via a common infrastructure, CUBEnet uses state of the art sensors to provide a set of networked measurements systems, visualization tools, and model developments to gain an understanding of the Gulf of Mexico’s marine environments. CUBEnet is also a mechanism for improved human engagement with Gulf of Mexico resources and provides stake holders with the data needed to make informed coastal, environmental, and economic decisions. The Coastal CUBEnet’s data environment utilizes both stationary and uncrewed mobile systems and high-resolution distributed sensors to create a networked platform across the northern coastal Gulf of Mexico. CUBEnet’s modeling environment has developed an implementation of The Coupled Ocean Atmosphere Wave Sediment Transport (COAWST) Model for the Mississippi Bight region that has been applied to investigate shore to shelf advective exchange processes, and their influence on coastal water quality conditions that support the region’s prolific marine ecosystem. CUBEnet’s modeling environment provides prototype modeling applications that are supported by real-time observations of key coastal environmental variables. CUBEnet’s Web accessible visualization tools provide parameter fields and vertical profiles from hydrodynamic models and field observations. Nowcasts and forecast results are available for the Eastern LA coastline, MS coastline, Mobile Bay, and the West coast of Florida.

Topological abnormalities of the morphometric similarity network of the cerebral cortex in schizophrenia

A morphometric similarity (MS) network can be constructed using multiple magnetic resonance imaging parameters of each cortical region. An MS network can be used to assess the similarity between cortical regions. Although MS networks can detect microstructural alterations and capture connections between histologically similar cortical areas, the influence of schizophrenia on the topological characteristics of MS networks remains unclear. We obtained T1- and diffusion-weighted images of 239 healthy controls and 190 individuals with schizophrenia to construct the MS network. Group comparisons of the mean MS of the cortical regions and subnetworks were performed. The strengths of the connections between the cortical regions and the global and nodal network indices were compared between the groups. Clinical associations with the network indices were tested using Spearman’s rho. Compared with healthy controls, individuals with schizophrenia had significant group differences in the mean MS of several cortical regions and subnetworks. Individuals with schizophrenia had both superior and inferior strengths of connections between cortical regions compared with those of healthy controls. We observed regional abnormalities of the MS network in individuals with schizophrenia regarding lower centrality values of the pars opercularis, superior frontal, and superior temporal areas. Specific nodal network measures of the right pars opercularis and left superior temporal areas were associated with illness duration in individuals with schizophrenia. We identified regional abnormalities of the MS network in schizophrenia with the left superior temporal area possibly being a key region in topological organization and cortical connections.

Mechanistic and Statistical Analysis of Possible Defense Mechanisms for Probabilistic Failures in Robustness, Stability and Sustainability of Static and Dynamic Complex Networks

Complex networks are ubiquitous even in the form of defense networks in many fields such as ecological immune systems, biological immune systems, brain networks, air defense systems, national security, cyber security, peace and conflict studies, and so on. In the real world, complex networks exist in both static and dynamic forms. By understanding probabilistic failures and their possible defense mechanisms in static and dynamic complex networks, scientists and researchers can make decisions in developing and implementing methodologies of many fields as we can say, from the fields of biology to fields of technology. In this research, static and dynamic scale-free and small world networks have been generated as complex networks with network generation algorithms. Besides that, probabilistic failures and their possible defense mechanisms have also been generated and designed for previously generated complex networks. On these generated probabilistic failures and their possible defense mechanism, several mechanistic and statistical analysis methods have been applied. To generate these static and dynamic complex networks and their probabilistic failures and possible defense mechanism, two modified network generation algorithms along with several network measurements algorithms have been implemented where parameters have been chosen to produce possible types of static and dynamic scale-free and small world networks, and their corresponding probabilistic failures and possible defense mechanisms. Besides analyzing probabilistic failures and possible defense mechanisms for generated complex networks; the robustness, stability and sustainability of those networks have been studied and analyzed for probabilistic failures and their possible defense mechanisms.

Seeding success: Integrating seed dispersal networks in tropical forest restoration

AbstractWhile the reassembly of fruit‐frugivore interactions remains at the forefront of tropical forest restoration, seed dispersal networks emerge as a potential approach to enhance restoration success. This review explores the integration of seed dispersal networks in tropical forest restoration, with the aims of (1) synthesizing important findings in the literature, (2) detailing potential biases in utilizing network theory, and (3) addressing current knowledge gaps and future directions for the field. We first highlight the importance of combining phytocentric and zoocentric approaches when sampling for seed dispersal interactions, as different methodologies have varying effects on network measures, and combining approaches can foster a more comprehensive understanding of dispersal interactions. Furthermore, when integrating seed dispersal networks into restoration goals, we suggest a highly connected and species‐rich network is desirable for earlier stages of forest succession where community turnover and transient interactions are pivotal. Nested patterns may emerge throughout varying stages of forest succession, and identifying generalist species that make up nested patterns may be useful for restoration practitioners in both early and later stages of forest regeneration. Modularity should be highest at later stages of succession to maintain community structure and stability, and connector species may play important roles in facilitating seed dispersal across temporal scales. Finally, we emphasize the importance of site‐specific long‐term datasets, chronosequences, and studies at large spatial scales to continue to understand network reassembly as a function of tropical forest succession and to develop effective strategies that enhance the recovery of tropical forest ecosystems.

A psychological network analysis of the relationship among component importance measures

Importance measures (IMs) in networks are indices that allow the analysis and evaluation of the network components that are most critical to the performance of the network. Such information is useful for a decision-maker as it enables taking actions to prevent or improve the performance of the network in the face of changing operational events (e.g., the identification of important links that should be hardened or made redundant). This paper presents an approach to analyze the relationships between the IMs through the use of so-called psychological networks, which estimate the characteristics of a new kind of network wherein the “nodes” correspond to IMs and the connecting links and their capacities are derived statistically using the IMs calculated. Such estimation does not use any a priori information of relationships among IMs. The approach proposed in this work defines an equivalence paradigm not described previously in the literature between the approach used in psychology and the IMs used to measure networks. As a result, the main characteristics of the relationships among IMs are derived, such as magnitude, sign, and robustness of the selected IMs. An example related to a transportation network and a set of flow-based IMs illustrates the contribution of psychological networks for understanding how the IMs interact.

HSS: enhancing IoT malicious traffic classification leveraging hybrid sampling strategy

Using deep learning models to deal with the classification tasks in network traffic offers a new approach to address the imbalanced Internet of Things malicious traffic classification problems. However, the employment difficulty of these models may be immense due to their high resource consumption and inadequate interpretability. Fortunately, the effectiveness of sampling methods based on the statistical principles in imbalance data distribution indicates the path. In this paper, we address these challenges by proposing a hybrid sampling method, termed HSS, which integrates undersampling and oversampling techniques. Our approach not only mitigates the imbalance in malicious traffic but also fine-tunes the sampling threshold to optimize performance, as substantiated through validation tests. Employed across three distinct classification tasks, this method furnishes simplified yet representative samples, enhancing the baseline models’ classification capabilities by a minimum of 6.02% and a maximum of 182.66%. Moreover, it notably reduces resource consumption, with sample numbers diminishing to a ratio of at least 83.53%. This investigation serves as a foundation, demonstrating the efficacy of HSS in bolstering security measures in IoT networks, potentially guiding the development of more adept and resource-efficient solutions.

An approach to complex network analysis on pp collisions at LHC energies

We have introduced the complex network analysis techniques such as Visibility Graph (VG), Horizontal Visibility Graph (HVG) and Sandbox (SB) algorithm as a mapping between complex network and time series measurements. This study explores the application of complex network-based analysis techniques to proton–proton [Formula: see text] collisions data at [Formula: see text], 7, 5.02 and 2.76[Formula: see text][Formula: see text]TeV to unravel the underlying dynamics of the particles and their interactions. To ensure the robustness and comprehensive analysis, we have incorporated UrQMD-, PYTHIA- and AMPT-simulated data sets of [Formula: see text] interactions at LHC energies that enable a thorough investigation of the underlying phenomena of the interactions. We have analyzed the fluctuations in pseudorapidity densities of singly charged particles created in [Formula: see text] interactions at LHC energies. We have also investigated some properties like symmetry scaling, correlation, clustering and scale-freeness among the produced particles. The findings from this study contribute to our understanding of the complex dynamics within [Formula: see text] collisions and have implications for the field of high-energy physics and particle phenomenology.

Learning-Based Sketch for Adaptive and High-Performance Network Measurement

Learning-Based Sketch for Adaptive and High-Performance Network Measurement

Dynamic Reconfiguration of Brain Functional Network in Stroke.

The brain continually reorganizes its functional network to adapt to post-stroke functional impairments. Previous studies using static modularity analysis have presented global-level behavior patterns of this network reorganization. However, it is far from understood how the brain reconfigures its functional network dynamically following a stroke. This study collected resting-state functional MRI data from 15 stroke patients, with mild (n = 6) and severe (n = 9) two subgroups based on their clinical symptoms. Additionally, 15 age-matched healthy subjects were considered as controls. By applying a multilayer temporal network method, a dynamic modular structure was recognized based on a time-resolved function network. The dynamic network measurements (recruitment, integration, and flexibility) were calculated to characterize the dynamic reconfiguration of post-stroke brain functional networks, hence, revealing the neural functional rebuilding process. It was found from this investigation that severe patients tended to have reduced recruitment and increased between-network integration, while mild patients exhibited low network flexibility and less network integration. It's also noted that previous studies using static methods could not reveal this severity-dependent alteration in network interaction. Clinically, the obtained knowledge of the diverse patterns of dynamic adjustment in brain functional networks observed from the brain neuronal images could help understand the underlying mechanism of the motor, speech, and cognitive functional impairments caused by stroke attacks. The present method not only could be used to evaluate patients' current brain status but also has the potential to provide insights into prognosis analysis and prediction.

Description and analysis of Sigfox received signal strength indicator dataset by using statistical techniques

Low power wide area network (LPWAN) technology has expanded and is essential in the development of applications for the internet of things (IoT). The Sigfox LPWAN network is characterized by its long-range coverage, low cost and power consumption. In this article, a set of 5174 values is analyzed, containing 1606 null RSSI data, obtained with the Sipy module and MicroPython, which provide a coverage map of several points with a resolution of 200 meters deployed in Quito–Ecuador. It is evaluated the type of distribution to which the set of network measurements is adjusted and an optimal 900 MHz propagation model in suburban environments is determined from the measurements obtained from the known base station. As a result, the lost values of RSSI were predicted using the inverse normal distribution method in the original values, observing that they conform to a logistic distribution. The data from the base station were subjected to a data augmentation algorithm designed in MATLAB, determining that the stanford university interim (SUI) model reduces the precision error in the trend of the curve by not presenting changes greater than 5 dB, achieving a precision of 97% with respect to the fit of the curve of the data.

Do board networks matter for corporate cash holdings? Evidence from Australian firms

PurposeIn today’s interconnected world, social capital has emerged as a crucial business competence, drawing significant attention in recent literature. Using social network analysis, this study aims to investigate the impact of network centrality, established by a firm through its board members (i.e. boardroom networks), on corporate cash holdings.Design/methodology/approachThis study uses extensive panel data comprising 36,963 firm-year observations of firms listed on the Australian Securities Exchange, spanning a 22-year period (2001–2022). The study uses firm fixed-effect regression along with several alternative specifications and an instrumental variable approach to ensure the robustness of the results. Boardroom network centrality is quantified by five measures that capture different perspectives on networks as viable conduits for resource exchange and information flow: degree, two-step reach, closeness, eigenvector and betweenness.FindingsThe authors find evidence for the benefits of board networks. Firms with well-connected boards (central firms) are more likely to have smaller cash holdings. The findings also reveal distinct effects stemming from local and global properties of centrality, with local network measures playing a more pronounced role in shaping cash-holding decisions. Overall, the evidence reflects the ability of connected directors to enhance governance by limiting managerial discretion over cash reserves, thus reducing agency conflicts associated with cash holdings.Research limitations/implicationsThis study offers important insights for regulators, investors and practitioners, highlighting the potential for connected directors to effectively curtail managerial autonomy in deploying corporate cash holdings.Originality/valueThis study contributes to the ongoing discussion about the advantages and drawbacks of board networks, which constitute a vibrant and burgeoning area of research in the finance literature. It also complements scanty network-based studies on firm cash holdings. Importantly, this study extends prior work by providing robust evidence and a comprehensive analysis of the nuanced roles that board networks play in affecting the level of cash reserves.

An indirect estimation of x-ray spectrum via convolutional neural network and transmission measurement

Objective. In this work, we aim to propose an accurate and robust spectrum estimation method by synergistically combining x-ray imaging physics with a convolutional neural network (CNN). Approach. The approach relies on transmission measurements, and the estimated spectrum is formulated as a convolutional summation of a few model spectra generated using Monte Carlo simulation. The difference between the actual and estimated projections is utilized as the loss function to train the network. We contrasted this approach with the weighted sums of model spectra approach previously proposed. Comprehensive studies were performed to demonstrate the robustness and accuracy of the proposed approach in various scenarios. Main results. The results show the desirable accuracy of the CNN-based method for spectrum estimation. The ME and NRMSE were −0.021 keV and 3.04% for 80 kVp, and 0.006 keV and 4.44% for 100 kVp, superior to the previous approach. The robustness test and experimental study also demonstrated superior performances. The CNN-based approach yielded remarkably consistent results in phantoms with various material combinations, and the CNN-based approach was robust concerning spectrum generators and calibration phantoms. Significance. We proposed a method for estimating the real spectrum by integrating a deep learning model with real imaging physics. The results demonstrated that this method was accurate and robust in estimating the spectrum, and it is potentially helpful for broad x-ray imaging tasks.

The small-world effect for interferometer networks

Complex network theory has focused on properties of networks with real-valued edge weights. However, in signal transfer networks, such as those representing the transfer of light across an interferometer, complex-valued edge weights are needed to represent the manipulation of the signal in both magnitude and phase. These complex-valued edge weights introduce interference into the signal transfer, but it is unknown how such interference affects network properties such as small-worldness. To address this gap, we have introduced a small-world interferometer network model with complex-valued edge weights and generalized existing network measures to define the interferometric clustering coefficient, the apparent path length, and the interferometric small-world coefficient. Using high-performance computing resources, we generated a large set of small-world interferometers over a wide range of parameters in system size, nearest-neighbor count, and edge-weight phase and computed their interferometric network measures. We found that the interferometric small-world coefficient depends significantly on the amount of phase on complex-valued edge weights: for small edge-weight phases, constructive interference led to a higher interferometric small-world coefficient; while larger edge-weight phases induced destructive interference which led to a lower interferometric small-world coefficient. Thus, for the small-world interferometer model, interferometric measures are necessary to capture the effect of interference on signal transfer. This model is an example of the type of problem that necessitates interferometric measures, and applies to any wave-based network including quantum networks.

Web Application to Enable Online Social Interactions in a Parkinson Disease Risk Cohort: Feasibility Study and Social Network Analysis.

There is evidence that social interaction has an inverse association with the development of neurodegenerative diseases. PREDICT-Parkinson Disease (PREDICT-PD) is an online UK cohort study that stratifies participants for risk of future Parkinson disease (PD). This study aims to explore the methodological approach and feasibility of assessing the digital social characteristics of people at risk of developing PD and their social capital within the PREDICT-PD platform, making hypotheses about the relationship between web-based social engagement and potential predictive risk indicators of PD. A web-based application was built to enable social interaction through the PREDICT-PD portal. Feedback from existing members of the cohort was sought and informed the design of the pilot. Dedicated staff used weekly engagement activities, consisting of PD-related research, facts, and queries, to stimulate discussion. Data were collected by the hosting platform. We examined the pattern of connections generated over time through the cumulative number of posts and replies and ego networks using social network analysis. We used network metrics to describe the bonding, bridging, and linking of social capital among participants on the platform. Relevant demographic data and Parkinson risk scores (expressed as an odd 1:x) were analyzed using descriptive statistics. Regression analysis was conducted to estimate the relationship between risk scores (after log transformation) and network measures. Overall, 219 participants took part in a 4-month pilot forum embedded in the study website. In it, 200 people (n=80, 40% male and n=113, 57% female) connected in a large group, where most pairs of users could reach one another either directly or indirectly through other users. A total of 59% (20/34) of discussions were spontaneously started by participants. Participation was asynchronous, with some individuals acting as "brokers" between groups of discussions. As more participants joined the forum and connected to one another through online posts, distinct groups of connected users started to emerge. This pilot showed that a forum application within the cohort web platform was feasible and acceptable and fostered digital social interaction. Matching participants' web-based social engagement with previously collected data at individual level in the PREDICT-PD study was feasible, showing potential for future analyses correlating online network characteristics with the risk of PD over time, as well as testing digital social engagement as an intervention to modify the risk of developing neurodegenerative diseases. The results from the pilot suggest that an online forum can serve as an intervention to enhance social connectedness and investigate whether patterns of online engagement can impact the risk of developing PD through long-term follow-up. This highlights the potential of leveraging online platforms to study the role of social capital in moderating PD risk and underscores the feasibility of such approaches in future research or interventions.

The role of elasticity on adhesion and clustering of neurons on soft surfaces

The question of whether material stiffness enhances cell adhesion and clustering is still open to debate. Results from the literature are seemingly contradictory, with some reports illustrating that adhesion increases with surface stiffness and others suggesting that the performance of a system of cells is curbed by high values of elasticity. To address the role of elasticity as a regulator in neuronal cell adhesion and clustering, we investigated the topological characteristics of networks of neurons on polydimethylsiloxane (PDMS) surfaces - with values of elasticity (E) varying in the 0.55–2.65 MPa range. Results illustrate that, as elasticity increases, the number of neurons adhering on the surface decreases. Notably, the small-world coefficient – a topological measure of networks – also decreases. Numerical simulations and functional multi-calcium imaging experiments further indicated that the activity of neuronal cells on soft surfaces improves for decreasing E. Experimental findings are supported by a mathematical model, that explains adhesion and clustering of cells on soft materials as a function of few parameters - including the Young’s modulus and roughness of the material. Overall, results indicate that – in the considered elasticity interval – increasing the compliance of a material improves adhesion, improves clustering, and enhances communication of neurons.

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.

The relationship among generalized trust, social networks, and social resources across 30 countries

The study proposes an integrative model of individual social capital and examines if trust, social networks, and social resources are related across countries. Although trust and social resources were often referred to as social capital or its essential components, the literature lacks empirical verification of whether and how they are associated. Particularly, examining the relationship by a specific measurement of social networks is imperative. The relationship should be identified considering the influences of country-level contingencies. The study applied a multilevel within-between mixed regression method to the International Social Survey Program 2017 data from 30 countries. Using a position generator of social networks, the study found that generalized trust was associated with interpersonal networks primarily through weak ties across countries, accounting for country-level contingencies. Both strong and weak ties were instrumental in embedding social resources. The results supported the integrative model of social capital that connects generalized trust to social resources.

Quality in care homes: How wearable devices and social network analysis might help.

Social network analysis can support quality improvement in care homes but traditional approaches to social network analysis are not always feasible in care homes. Recalling contacts and movements in a home is difficult for residents and staff and documentary and other sources of individual contacts can be unreliable. Bluetooth enabled wearable devices are a potential means of generating reliable, trustworthy, social network data in care home communities. In this paper, we explore the empirical, theoretical and real-world potential and difficulties in using Bluetooth enabled wearables with residents and staff in care homes for quality improvement. We demonstrate, for the first time, that a relatively simple system built around the Internet of Things, Bluetooth enabled wearables for residents and staff and passive location devices (the CONTACT intervention) can capture social networks and data in homes, enabling social network analysis, measures, statistics and visualisations. Unexpected variations in social network measures and patterns are surfaced, alongside "uncomfortable" information concerning staff time spent with residents. We show how technology might also help identify those most in need of social contact in a home. The possibilities of technology-enabled social network analysis must be balanced against the implementation-related challenges associated with introducing innovations in complex social systems such as care homes. Behavioural challenges notwithstanding, we argue that armed with social network information, care home staff could better tailor, plan and evaluate the effects of quality improvement with the sub-communities that make up a care home community.

Disrupted single-subject gray matter networks are associated with cognitive decline and cortical atrophy in Alzheimer's disease.

Research has shown disrupted structural network measures related to cognitive decline and future cortical atrophy during the progression of Alzheimer's disease (AD). However, evidence regarding the individual variability of gray matter network measures and the associations with concurrent cognitive decline and cortical atrophy related to AD is still sparse. To investigate whether alterations in single-subject gray matter networks are related to concurrent cognitive decline and cortical gray matter atrophy during AD progression. We analyzed structural MRI data from 185 cognitively normal (CN), 150 mild cognitive impairment (MCI), and 153 AD participants, and calculated the global network metrics of gray matter networks for each participant. We examined the alterations of single-subject gray matter networks in patients with MCI and AD, and investigated the associations of network metrics with concurrent cognitive decline and cortical gray matter atrophy. The small-world properties including gamma, lambda, and sigma had lower values in the MCI and AD groups than the CN group. AD patients had reduced degree, clustering coefficient, and path length than the CN and MCI groups. We observed significant associations of cognitive ability with degree in the CN group, with gamma and sigma in the MCI group, and with degree, connectivity density, clustering coefficient, and path length in the AD group. There were significant correlation patterns between sigma values and cortical gray matter volume in the CN, MCI, and AD groups. These findings suggest the individual variability of gray matter network metrics may be valuable to track concurrent cognitive decline and cortical atrophy during AD progression. This may contribute to a better understanding of cognitive decline and brain morphological alterations related to AD.

Functional brain network analysis using electroencephalography in late-onset Lennox–Gastaut syndrome

ObjectiveWe aimed to explore the clinical characteristics and functional network properties of patients with late-onset Lennox–Gastaut syndrome (LGS). MethodsLate-onset LGS was defined by the appearance of LGS features after 8 years of age. We reviewed the medical charts of 9 patients with late-onset LGS, and performed electroencephalography connectivity analysis using graph theory. We assessed the clustering coefficient (CC) and characteristic path length (CPL), which are common basic measures of functional networks that represent local segregation and global integration. The characteristics and brain parameters of late-onset LGS were compared with a typical age-onset LGS group. ResultsLate onset LGS subjects were older than typical age onset LGS at the time of testing, but otherwise there were no significant differences in clinical characteristics. The late-onset group showed higher median CC values in the alpha (p = 0.045) and beta (p < 0.001) bands over brain regions implicated in cognitive processing. There were no significant differences in CPL between the LGS groups. ConclusionsHigher clustering coefficient values, in alpha/beta bands over brain regions implicated in cognitive processing, are consistent with increased cognitive network segregation in late onset LGS compared to typical age-onset LGS. Given network segregation is a normal aspect of brain maturation, these results imply that this process is less disturbed when the LGS process begins later in childhood.