Network Betweenness Centrality Research Articles

Variability of large timescale functional networks in patients with disorders of consciousness.

Most brain function assessments for disorders of consciousness (DOC) utilized quantified characteristics, measured only once, ignoring the variation of patients' brain states. The study aims to investigate the brain activities of patients with DOC from a new perspective: variability of a large timescale functional network. Forty-nine patients were enrolled in this study and performed a 1-week behavioral assessment. Subsequently, each patient received electroencephalography (EEG) recordings five times daily at 2-h intervals. Functional connectivity and networks were measured by weighted phase lag index and complex network parameters (characteristic path length, cluster coefficient, and betweenness centrality). The relative coefficient of variation (CV) of network parameters was measured to evaluate functional network variability. Functional networks of patients with vegetative state/unresponsive wakefulness syndrome (VS/UWS) showed significantly higher segregation (characteristic path length) and lower centrality (betweenness centrality) than emerging from the minimal conscious state (EMCS) and minimal conscious state (MCS), as well as lower integration (cluster coefficient) than MCS. The functional networks of VS/UWS patients consistently presented the highest variability in segregation and integration (i.e., highest CV values of characteristic path length and cluster coefficient) on a larger time scale than MCS and EMCS. Moreover, the CV values of characteristic path length and cluster coefficient showed a significant inverse correlation with the Coma Recovery Scale-Revised scores (CRS-R). The CV values of network betweenness centrality, particularly of the cento-parietal region, showed a positive correlation with the CRS-R. The functional networks of VS/UWS patients present the most invariant segregation and integration but divergent centrality on the large time scale networks than MCS and EMCS. The variations observed within large timescale functional networks significantly correlate with the degree of consciousness impairment. This finding augments our understanding of the neurophysiological mechanisms underpinning disorders of consciousness.

Network and meta-omics reveal the cooperation patterns and mechanisms in an efficient 1,4-dioxane-degrading microbial consortium.

1,4-Dioxane is an emerging wastewater contaminant with probable human carcinogenicity. Our current understanding of microbial interactions during 1,4-dioxane biodegradation process in mixed cultures is limited. Here, we applied metagenomic, metatranscriptomic and co-occurrence network analyses to unraveling the microbial cooperation between degrader and non-degraders in an efficient 1,4-dioxane-degrading microbial consortium CH1. A 1,4-dioxane-degrading bacterium, Ancylobacter polymorphus ZM13, was isolated from CH1 and had a potential of being one of the important degraders due to its high relative abundance, highly expressed monooxygenase genes tmoABCDEF and high betweenness centrality of networks. The strain ZM13 cooperated obviously with 6 bacterial genera in the network, among which Xanthobacter and Mesorhizobium could be involved in the intermediates metabolism with responsible genes encoding alcohol dehydrogenase (adh), aldehyde dehydrogenase (aldh), glycolate oxidase (glcDEF), glyoxylate carboligase (gcl), malate synthase (glcB) and 2-isopropylmalate synthase (leuA) differentially high-expressed. Also, 1,4-dioxane facilitated the shift of biodiversity and function of CH1, and those cooperators cooperated with ZM13 in the way of providing amino acids or fatty acids, as well as relieving environmental stresses to promote biodegradation. These results provide new insights into our understandings of the microbial interactions during 1,4-dioxane degradation, and have important implications for predicting microbial cooperation and constructing efficient and stable synthetic 1,4-dioxane-degrading consortia for practical remediation.

Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis

Cycling benefits both the individual and society in terms of public health promotion, traffic congestion relief and vehicle emissions reduction. To better understand cycling behaviors, we analyze non-linear relationships and interaction effects between the built environment and cycling distance. Few studies explore the interaction effects on cycling distance in which road network patterns interact with the demographic, trip, and other built environment characteristics to produce complex effects. Previous research has not examined the effect size or relative contribution of various variables have on cycling distance. Thus, this study adopts eXtreme Gradient Boosting (XGBoost) to examine the non-linear relationships among road network patterns, demographic, trip, and bike lane infrastructure, and other built environment characteristics and cycling distance, and employs SHapley Additive exPlanations (SHAP) to discover complex interaction effects on cycling distance, based on a bike travel survey in Xi'an, China. The results show that road network patterns have the greatest contribution; bike lane infrastructure is also quite important and has larger collective contributions than land use patterns and socioeconomics. Average geodesic distance and network betweenness centrality, two topological indices to describe road network structure, interact with bike lane infrastructure, land use and demographic characteristics to produce interaction effects on explaining cycling behaviors. When the average geodesic distance is smaller than 2.8 and the network betweenness centrality is smaller than 50%; as average geodesic distance increases, the network betweenness centrality has a positive effect on cycling distance. A network with a lower average geodesic distance, and with a higher intersection density makes cyclists ride a longer distance. A network with a higher value of average geodesic distance discourages cyclists to detour.

The role of organizational networks in ameliorating flood disaster impacts: A case study of flood inundated rural and urban areas in Sri Lanka

In order to better align studies of Sri Lankan disaster management with contemporary theorizing and extant research results, this contribution provides a first-of-its-kind comparison of the structure and efficacy of social and organizational network legacies vis-à-vis flood disaster management in both rural and urban areas in Sri Lanka. A mixed methods approach is deployed. Results are based on quantitative analyses of survey data from 52 government and non-government stakeholders, in addition to qualitative input obtained during five focus group interviews and nine unstructured interviews with villagers. Social network analysis (SNA), conducted via UCINET software, examines the degree centrality, closeness centrality, and betweenness centrality of networks in three district secretariat divisions (DSDs), namely Colombo, Elapatha, and Kuruwita, areas that experienced mass flooding events in 2016 and 2017. Results suggest that the rural Kuruwita and Elapatha DSDs demonstrated denser organizational networks respectively compared to the urban Colombo DSD. This in turn led to differentiated perceptions of relief efficacy. In terms of social capital applications for disaster management studies, results suggest that bridging, bonding, and institutional considerations, along with the disaster relief expectations generated as a result, led to the creation of richer interpersonal ties and community-level organizations, particularly pronounced in rural areas, that worked in concert with broader formal networks to better address flood inundations. The study demonstrates that the development and mobilization of various actors and resources through social and organizational networks matter when ameliorating flood disaster impacts, thus imparting lessons for Sri Lanka's capital region as well as potentially areas in other countries similarly afflicted.

Resting-State MEG Source Space Network Metrics Associated with the Duration of Temporal Lobe Epilepsy.

To evaluate the relationship between the network metrics of 68 brain regions and duration of temporal lobe epilepsy (TLE). Magnetoencephalography (MEG) data from 53 patients with TLE (28 left TLE, 25 right TLE) were recorded between seizures at resting state and analyzed in six frequency bands: delta (0.1-4Hz), theta (4-8Hz), lower alpha (8-10Hz), upper alpha (10-13Hz), beta (13-30Hz), and lower gamma (30-48Hz). Three local network metrics, betweenness centrality, nodal degree, and nodal efficiency, were chosen to analyze the functional brain network. In Left, Right, and All (Left + Right) TLE groups, different metrics provide significant positive or negative correlations with the duration of TLE, in different frequency bands, and in different brain regions. In the Left TLE group, significant correlation between TLE duration and metric exists in the delta, beta, or lower gamma band, with network betweenness centrality, nodal degree, or nodal efficiency, in left caudal middle frontal, left middle temporal, or left supramarginal. In the Right TLE group, significant correlation exists in lower gamma or delta band, with nodal degree, or nodal efficiency, in left precuneus or right temporal pole. In the All TLE group, the significant correlation exists in delta, theta, beta, or lower gamma band, with nodal degree, or betweenness centrality, in either left or right hemisphere. Network metrics for some specific brain regions changed in patients with TLE as the duration of their TLE increased. Further researching these changes may be important for studying the pathogenesis, presurgical evaluation, and clinical treatment of long-term TLE.

Patent Analysis on the Development of the Shale Petroleum Industry Based on a Network of Technological Indices

This study investigated the technological developments in the shale petroleum industry by analyzing patent data using a network of technological indices. The technological developments were promoted by the beginning of the shale industry, and after the first five years, it showed a more complex development pattern with the convergence of critical technologies. This paper described progress in the shale petroleum technologies as changes in relatedness networks of technological components. The relatedness represents degree of convergence between technological components, and betweenness centrality of network represents priority of technological components. In the results, the progress of the critical technologies such as directional drilling, increasing permeability, and smart systems, were actively carried out from 2012 to 2016. Especially, unconverged technology of increasing permeability and the converged technology of directional drilling and smart system has been intensively developed. Some technological components of the critical technologies are more significant in the form of converged technology.

To See from Where You Stand: Supply Chain Network Positions and Merger and Acquisition Likelihood

Drawing on the proverb that says “where you stand determines what you see and how you do”, this study examines the effect of a firm’s supply chain network position on its M&As decisions and further examines the moderating effects of financial slack and innovational slack on the relationship between a firm’s supply chain network centrality and its M&As likelihood. Secondary data from different sources spanning from 2010 to 2017 are collected and analyzed to test our hypotheses. We find that there is U-shaped relationship between a firm’s supply chain network betweenness centrality and its M&As likelihood. Moreover, we also find significant evidence that the financial slack and innovational slack of a firm will positively moderate the effect of a firm’s supply chain network position on its M&As likelihood. This study advances extant literature on the antecedents of M&As by identifying supply chain network as a critical antecedent of a firm’s M&As. In addition, this study extends organizational slacks and M&As literature by identifying financial slack and innovational slack as contingencies to a firm’s M&As decision, which is different from conventional literature on slacks and M&As.

Can Urban Street Network Characteristics Indicate Economic Development Level? Evidence from Chinese Cities

The street network is considered the skeleton of the city structure; it determines the efficiency and productivity of the city in that it acts like blood vessels transporting people, goods, and information. The relationship between street networks and economic development is an important research topic in urban geography. In recent years, complex network theory has been successfully used for understanding the characteristics of street network structure. However, researchers lack an analytical framework and methods for studying the relationship between the morphological structure of urban streets and the economic development level of cities. Accordingly, this paper proposes a methodological framework for first, quantitatively characterizing the urban morphological structure based on open street network data, and second, exploring the relationship between the morphological structure of the urban street and the urban economic development level. The proposed methodology was applied to 31 provincial capital cities in China. The results indicate that urban morphological structure can be quantitatively described by betweenness and closeness centrality extracted from street networks. Cities with similar structures have similar levels of economic development. Moreover, the results suggest a significant positive correlation between street network betweenness centrality Gini coefficients and cities’ economic development levels, indicating that the street network may affect city productivity. This study makes two major contributions to the scholarly literature. Methodologically, the proposed framework provides technical and methodological support for a better understanding of the relationship between cities’ economic development and urban street structure. Empirically, the demonstrated case study may guide decision-making involving regional development and the optimization of urban space.

Measuring originality in science

Originality has self-evident importance for science, but objectively measuring originality poses a formidable challenge. We conceptualise originality as the degree to which a scientific discovery provides subsequent studies with unique knowledge that is not available from previous studies. Accordingly, we operationalise a new measure of originality for individual scientific papers building on the network betweenness centrality concept. Specifically, we measure the originality of a paper based on the directed citation network between its references and the subsequent papers citing it. We demonstrate the validity of this measure using survey information. In particular, we find that the proposed measure is positively correlated with the self-assessed theoretical originality but not with the methodological originality. We also find that originality can be reliably measured with only a small number of subsequent citing papers, which lowers computational cost and contributes to practical utility. The measure also predicts future citations, further confirming its validity. We further characterise the measure to guide its future use.

Reduced betweenness centrality of a sensory-motor vestibular network in subclinical agoraphobia.

Agoraphobic patients feel dizzy in crowded open spaces and respond to this symptom with excessive fear and avoidance. These clinical features show great similitude with the newly defined syndrome of persistent postural perceptual dizziness (PPPD). Patients with PPPD show decreased activity and connectivity in regions of the vestibular cortex. Due to the great overlap between these two conditions, we hypothesized that individuals with sub-clinical agoraphobia would show reduction in the connectivity features of these regions. We selected a group of healthy individuals from the Human Connectome Project that self-reported agoraphobia episodes, and compared it with a control group. We accurately matched the two groups for psychological measures and personality traits in order to study the neural correlates of vestibular symptoms independently of possible psychiatric vulnerabilities. We found that the agoraphobia group showed reduced betweenness centrality of a network encompassing key regions of the vestibular cortex. Dysfunctions of the vestibular cortex may explain the dizziness symptom for a disorder previously labelled as psychogenic.

Characteristics of Information Dissemination of Agricultural Safety Emergencies Based on Complex Network

Information dissemination of agricultural safety emergencies on the Internet has drawn wide attention of academia and industry. Topological properties of information dissemination network of agricultural safety emergency on the news websites based on complex network theory, such as degree distribution, diameter, average path length, centrality and betweenness centrality of network, is studied in this paper. The result shows that the information dissemination network of agricultural safety emergency on the news websites presents small world feature, which means the dissemination network has relative small average path length and high betweenness centrality.

An Analysis of Associations between Neighborhood Road Networks and Pedestrian Traffic Accidents : Focused on the TAAS (2012-2014) in Seoul, Korea

The complexity, connectivity, and continuity of road network structure affect the speed of vehicle and drivers‘ vision and driving behavior, which strongly determines pedestrian safety. Thus more objective and in-depth research is required to identify the association between road network and pedestrian safety. In this context, this study examines the relationship between neighborhood environment including road network structure and pedestrian safety using the TAAS (2012-2014) data. We utilize negative binominal regression model. Unlike previous studies, this study includes exposure variables such as pedestrian volume, population, and road congestion level and objective measures of road network structure such as average geodesic distance, network betweenness centrality, average clustering coefficient. Key finding and implications can be organized as follows. First, pedestrian traffic accidents on community living zone road is strongly associated to road network structure of neighborhood-level. Second, we confirm that pedestrian volume and traffic condition are important variables for pedestrian accidents. Third, pedestrian traffic accidents are strongly associated with commercial land use and public transportation systems including median exclusive bus station. This study suggests policy implications for neighborhood road network design in urban planning and design projects and for pedestrian safety.

The Effects of an APOE Promoter Polymorphism on Human White Matter Connectivity during Non-Demented Aging.

Polymorphisms of the apolipoprotein E (APOE) promoter rs405509 are related to Alzheimer's disease (AD). The T/T allele of rs405509 decreases the transcription of the APOE gene and leads to impairments in a specific brain structural network in aged individuals; thus, it is an important risk factor for AD. However, it remains unknown whether rs405509 affects white matter networks during aging. Here, we investigated the effect of the rs405509 genotype (T/T versus G-allele) on age-related brain white matter structural networks via construction of the graph theory-based structural connectome using diffusion MRI data in a large cohort. Network communication efficiency was quantified, along with the network's betweenness centrality (Bc), global efficiency, local efficiency, and shortest path length. Regarding cognition, TT carriers had significant negative correlations between age and memory performance and between age and executive functions. A network analysis showed that TT carriers had an accelerated age-related loss of Bc and that regional Bc decreased in the left inferior frontal gyrus pars opercularis, the left posterior cingulate cortex, the right inferior occipital gyrus (IOG.R), and the left angular gyrus (ANG.L). Additional brain-behavior relationship analyses showed that polymorphism of rs405509 and age have strong interaction effects on the association of nodal Bc and cognition, mainly in the IOG.R and ANG.L. These results demonstrate that the rs405509 T/T allele of APOE causes an age-related cognitive decline in non-demented elderly people, possibly by modulating brain network communication efficiency, which may be beneficial for understanding the neural mechanisms of rs405509-related cognitive aging and AD pathogenesis.

Investigating the associations between road network structure and non-motorist accidents

Road networks channel traffic flow and can impact the volume and proximity of walking and bicycling. Therefore, the structure of road networks—the pattern by which roads are connected—can affect the safety of non-motorized road users. To understand the impact of roads’ structural features on pedestrian and bicyclist safety, this study analyzes the associations between road network structure and non-motorist-involved crashes using data from 321 census tracts in Alameda County, California. Average geodesic distance, network betweenness centrality, and an overall clustering coefficient were calculated to quantify the structure of road networks. Three statistical models were developed using the geographically weighted regression (GWR) technique for the three structural factors, in addition to other zonal factors including traffic behavior, land use, transportation facility, and demographic features. The results indicate that longer average geodesic distance, higher network betweenness centrality, and a larger overall clustering coefficient were related to fewer non-motorist-involved accidents. Thus, results suggest that: (1) if a network is more highly centered on major roads, there will be fewer non-motorist-involved crashes; (2) a network with a greater average number of intersections on the shortest path connecting each pair of roads tends to experience fewer crashes involving pedestrians and bicyclists; and (3) the more clustered road networks are into several sub-core networks, the lower the non-motorist crash count. The three structural measurements can reflect the configuration of a network so that it can be used in other network analyses. More information about the types of road network structures that are conducive to non-motorist traffic safety can help to guide the design of new networks and the retrofitting of existing networks. The estimation results of GWR models explain the spatial heterogeneity of correlations between explanatory factors and non-motorist crashes, which can support regional agencies in establishing local safety policies.

Differences in graph theory functional connectivity in left and right temporal lobe epilepsy

To investigate lateralized differences in limbic system functional connectivity between left and right temporal lobe epilepsy (TLE) using graph theory. Interictal resting state fMRI was performed in 14 left TLE patients, 11 right TLE patients, and 12 controls. Graph theory analysis of 10 bilateral limbic regions of interest was conducted. Changes in edgewise functional connectivity, network topology, and regional topology were quantified, and then left and right TLE were compared. Limbic edgewise functional connectivity was predominantly reduced in both left and right TLE. More regional connections were reduced in right TLE, most prominently involving reduced interhemispheric connectivity between the bilateral insula and bilateral hippocampi. A smaller number of limbic connections were increased in TLE, more so in left than in right TLE. Topologically, the most pronounced change was a reduction in average network betweenness centrality and concurrent increase in left hippocampal betweenness centrality in right TLE. In contrast, left TLE exhibited a weak trend toward increased right hippocampal betweenness centrality, with no change in average network betweenness centrality. Limbic functional connectivity is predominantly reduced in both left and right TLE, with more pronounced reductions in right TLE. In contrast, left TLE exhibits both edgewise and topological changes that suggest a tendency toward reorganization. Network changes in TLE and lateralized differences thereof may have important diagnostic and prognostic implications.

The Role of Ego Networks in Manufacturing Joint Venture Formations

This article develops a network theory–based framework of manufacturing joint venture formations and provides an empirical test in the context of the automotive industry. Hypotheses are developed regarding the implications of the network structure for a firm's partner selection in manufacturing joint ventures. The roles of network theory constructs such as ego network size, ego network density, and ego network betweenness centrality on new manufacturing joint venture formations are explored using a dynamic framework. A comprehensive time series panel dataset with 3,247,124 observations containing the joint venture information of 1,158 automotive firms collectively engaging in 589 manufacturing joint ventures over 19 years is utilized to test the hypotheses. Results provide strong empirical support for the role of network structure in equity‐based partnership formation. Specifically, ego network size and ego network betweenness centrality of both the focal manufacturer and potential partner have significant effects on new manufacturing joint venture formations. Findings regarding the role of ego network density are mixed.

Centrality Characteristics of Road Network Patterns of Traffic Analysis Zones

Road network patterns can affect traffic performance, travel behavior, and traffic safety. Thus, a deep understanding of the properties of different network patterns can provide useful guidance for design and improvement of road systems. The aim of this study is to build a relationship between graphical and topological features of road network patterns of traffic analysis zones (TAZ) and, on the basis of this relationship, to offer a measure that can quantitatively distinguish different graphical pattern types. Toward this goal, a topological analysis measure, centrality, is applied to investigate road network patterns metrically at the TAZ level. First, 662 TAZ road networks are classified according to the graphical features of the networks; then different graphical features are calculated for centrality indices including network degree centrality, network betweenness centrality, and network closeness centrality. It is concluded that the network betweenness centrality is the best measure to distinguish and describe various TAZ road network patterns. Finally, the problem of how to assign a road that happens to be on the border of two adjacent TAZs is studied. A measure that can quantitatively describe and represent different road network patterns is offered. This measure could be useful for further evaluation of the possible effects of TAZ road network patterns on transportation.

An approach to research the topology of Chinese temperature sequence based on complex network

To analyze the dynamics of the temperature data, using homogenous partition of coarse graining process, the series of Chinese daily main temperature from 1961 to 2002 is transformed into symbolic sequences consisting of 5 characters {R,r,e,d,D}. The vertices of the temperature fluctuation network is 125 3-symbol strings (i.e., 125 fluctuation patterns in durations of 4 days), linked in the network's topology by time sequence. It contains Integrated information about interconnections and interactions between fluctuation patterns of temperature in network topology. Random fluctuant network and chaos fluctuant network by using random sequence and chaos sequence of Lorenz system were built. We calculated the dynamical statistics of the degrees and the distribution of degrees, clustering coefficient and the shortest path length, and compared the difference of these three sequences from the view point of network. The result is that. The main vertices of temperature fluctuant network generally contain the 3 characters R, r and e, and corresponding the background of global warming, the feature of temperature fluctuation is mainly ascending. On all accounts, the temperature fluctuant network is similar with chaos network in its dynamical statistics, but is markedly different from random network, which reflects the complexity of temperature fluctuation. And besides, some vertices in temperature fluctuant network have high betweenness centrality (BC), 4% of vertices bear 71.9% of betweenness centrality of networks, these vertices of importance in topological statistics are helpful to understanding the inherent law and information transmission. The vertices' BC in chaos network is similar with that of the temperature network, but vertices in random network almost have identical betweenness centrality. As a result, the patterns of temperature fluctuation corresponding to the process of weather change have similar property with chaos rather than the random fluctuation process.

An approach to Hang Seng Index in Hong Kong stock market based on network topological statistics

Using homogenous partition of coarse graining process, the time series of Hang Seng Index (HSI) in Hong Kong stock market is transformed into discrete symbolic sequences S={S1S2S3...}, Si ∈ (R, r, d, D). Weighted networks of stock market are constructed by vertices that are 16 2-symbol strings (i.e. 16 patterns of HSI variations), and encode stock market relevant information about interconnections and interactions between fluctuation patterns of HSI in networks topology. By means of the measurements of betweenness centrality (BC) in networks, we have at least obtained 3 highest betweenneess centrality uniform vertices in 2 order of magnitude of time subinterval scale, i.e. 18.7% vertices undertake 71.9% betweenness centrality of networks, showing statistical stability. These properties cannot be found in random networks; here vertices almost have identical betweenness centrality. By comparison to random networks, we conclude that Hong Kong stock market, rather than a random system, is statistically stable.