High Betweenness Centrality Research Articles

Statistical determination of crucial taxa indicative of pollution gradients in sediments of Lake Taihu, China

In order to accurately monitor the changes in a freshwater ecosystem in response to anthropogenic stressors, microbe–environment correlations and microbe–microbe interactions were combined to determine crucial indicator taxa in contaminated sediments. The diversity, composition, and co–occurrence pattern of bacterial communities in 23 sediment samples collected from Lake Taihu were explored using 16S rRNA amplicon sequencing analysis. Fisher's exact test showed that the cluster analyses of samples could show a direct correlation between the relative abundance of bacterial communities and the physicochemical properties of the sediment (P < 0.0001), suggesting that bacterial communities can be used to monitor contamination gradients in freshwater sediments. According to the microbe–environment correlation, 24 orders and 60 families were initially identified via indicator species analysis as indicator taxa of different pollution levels. The co–occurrence network further showed that topological features of bacterial communities were clearly different at different pollution levels, although the diversity and composition of bacterial communities displayed similarities between minimally and moderately polluted sites. Indicator taxa were then screened for keystone species, which co–occurrence relationships showed the high degree and low betweenness centrality values (i.e. degree >5, betweenness centrality <1000) of the network. Nine orders and 13 families were finally extracted as crucial indicator taxa of the different pollution levels in eutrophic Lake Taihu. Obtaining crucial indicator taxa from environmental sequences allows to trace increasing levels of pollution in aquatic ecosystems and provides a novel mean to monitor watersheds sensitive to anthropic influences.

RETRACTED ARTICLE: Network evolution analysis of e-business entrepreneurship: big data analysis based on taobao intelligent information system

In recent years, the entrepreneurial development of taobao village based on e-business has led to a new round growth of rural economy in China. This paper constructs a evolution analysis framework of taobao village entrepreneurial network based on the big data of taobao intelligent information system from the year 2007–2016. The social network analysis software Gephi was applied to analyze the data. The result is that: Firstly, the early emerged online shops with high betweenness centrality degree, which mastered important resources and power within the network, formed a strong entrepreneurial demonstration effect to the other shops. Secondly, in taobao village, the number of strong relationship shops increased, entrepreneurial network gradually dispersed characteristics of clustering, entrepreneurial network community structure strength enhanced, division quality optimization, and the transmission performance and efficiency of entrepreneurial network improved. The entrepreneurial network has a typical characteristic of “acquaintance” network business conduction. Thirdly, the entrepreneurial network density of Lin’an taobao village showed a significant downward trend, which may be more conducive to the homogeneous agricultural products sales shops to access to heterogeneous resources, so as to obtain more long-term future development.

Framework for Assessing Resilience in the Communication Networks of AEC Teams

Architecture, engineering and construction (AEC) teams experience frequent changes in membership when delivering a project. While some of these changes are planned (e.g. phased involvement, role reassignment), others are unexpected (e.g. career change, sudden downsizing). Poorly planned or unexpected changes in membership can bring communication to a standstill and impair a team’s ability to deliver a successful project. By examining literature from civil engineering, as well as recent studies on network and team science, we propose a conceptual framework that places the formation, disruption and recovery cycle of communication networks in the context of a construction project. The purpose of this framework is to gain a better understanding of resilient communication networks at the project-level. To demonstrate the potential of this framework, email data was collected from the construction manager on an in-progress project that experienced an unexpected departure of their onsite project engineer. A week-byweek content and network analysis was performed for a period of one month to examine the impact of this change in team membership. The results suggest that the disruption in their communication network, measured as between a 40 to 50% loss in density, average degree and centralization following the departure of the Project Engineer, closely resembled the pattern of loss and recovery illustrated by the resilience triangle. Through discussion, the implications of these results at the project- and firm-level impacts are explored. Specifically, the framework demonstrated potential in forecasting vulnerability in a communication network by examining structural properties, such as high betweenness centrality relative to other members. However, we further that additional studies are needed to fully understand the dynamics and long-term benefits of more resilient communication networks, particularly as they relate to demographics and skillset of team members and the role of integrated delivery methods in promoting more robust networks.

Hepatic transcriptome of the euryhaline teleost Japanese seabass (Lateolabrax japonicus) fed diets characterized by α-linolenic acid or linoleic acid

To investigate the different effects of dietary α-linolenic acid (ALA) and linoleic acid (LA) on the euryhaline fish Japanese seabass, a feeding trial followed by hepatic transcriptome assay was conducted. Two experimental diets containing 10% LA-rich sunflower seed oil (diet LA) or 10% ALA-rich perilla oil (diet ALA) were used in the feeding trial. LA and ALA in diets were characteristically incorporated into fish tissues while no significant difference was observed in growth performance and body proximate composition between groups LA and ALA. Compared to LA, ALA up-regulated transcription of 49 unigenes and down-regulated those of 311 unigenes. Quantitative RT-PCR studies on eight lipid metabolism-related genes and seven randomly selected genes were conducted to validate the transcriptomic results. Lipid metabolism-related genes ApoA1, ApoA4, ApoE, FABP1, FABP3, FABP4, FATP6, and DGAT1, as well as ribosomal proteins L9e, L13e, and S4e, were transcriptionally down-regulated by ALA. The differentially expressed genes (DEGs) were primarily enriched in Gene Ontology terms such as Lipid transport, Protein metabolic process, and Ribosome biogenesis, as well as in KEGG pathways such as Complement and coagulation cascades and Ribosome. The Protein-Protein Interaction (PPI) network based on the peptide biosynthesis-related DEGs showed that ribosomal proteins such as SAe, L4e, S4e, L15e, L9e, and L13Ae had high betweenness centrality in the dietary regulation of peptide biosynthetic processes. In conclusion, under the present experimental conditions, a high level of dietary α-linolenic acid tended to suppress lipid transport and protein biosynthetic processes in the liver of Japanese seabass at the gene expression level.

Applying Social Network Analysis to Evaluate Implementation of a Multisector Population Health Collaborative That Uses a Bridging Hub Organization.

Background: Multisector collaboratives are increasingly popular strategies for improving population health. To be comprehensive, collaboratives must coordinate the activities of many organizations across a geographic region. Many policy-relevant models encourage creation and use of centralized hub organizations to do this work, yet there is little guidance on how to evaluate implementation of such hubs and track their network reach. We sought to demonstrate how social network analysis (SNA) could be used for this purpose.Methods: Through formative research, we defined and conceptualized key characteristics of a bridging hub network and identified a set of candidate measures—(1) network membership, (2) network interaction, (3) role and reach of the bridging hub, and (4) network collaboration—to evaluate its implementation within a pre-determined geographic region of Southeast Minnesota, USA. We then developed and administered a survey to assess outcomes as part of a SNA. We commented on the feasibility and usefulness of the methods.Results: The initial surveyed network consisted of 50 healthcare organizational sites and 50 community organizations representing sectors of public health, education, research, health promotion, social services, and long-term care and supports. Fifty-three of these organizations responded to the survey. The network's level of collaboration was “Cooperation” (level 2 of 5) and reported levels of collaboration varied by organization. Thirty-eight additional, unsurveyed organizations were identified as collaborators by respondents, pushing the theoretical network denominator up to 138 organizations. These additional organizations included grocery stores, ambulance services, and smaller, independent healthcare and community-based services focused on meeting the needs of underserved populations. The bridging hub organization had the highest betweenness centrality and was in good position to bridge healthcare and the community, although its organizational reach was estimated at only 51%. The SNA methods were feasible and useful for identifying opportunities and guiding implementation.Conclusions: Bridging hub organizations are not likely to link—or even be aware of—all relevant organizations in a geographic region at initial implementation. SNA may be a useful method for evaluating the value and reach of a bridging hub organization and guiding ongoing implementation efforts.Trial registration: http://ClinicalTrials.gov; #NCT03046498

Protein Network Analysis to Prioritize Key Genes and Pathway for Stress-Mediated Neurodegeneration

Background:Oxidative Stress (OS) has been implicated in the pathophysiology of many neurodegenerative diseases. OS can cause cellular damage that results in cell death due to overproduction of reactive oxygen species (ROS) that may play the crucial role in the disease progression. An impaired mechanism in correlation with reduced expression of antioxidant proteins is the very common feature among most of the age-related disorders. Variousin-vitroandin-vivostudies suggest the major contribution of oxidative stress in neurodegeneration. Role of Nrf2 gene is well established as a neuroprotective gene especially in concern with stress-mediated neurodegeneration. Nrf2 is a bZIP transcription factor that forms the heterodimer with small Maf protein and transcription factor AP1 that regulates transcription by binding to ARE which coordinates the transcription of genes involved in phase II detoxification and an antioxidant defense that is used to protect the cell from oxidative stress.Aim:The currentinsilicostudy was attempted to prioritize key genes and pathway in stress-mediated neurodegeneration through network-based analysis.Methods:Protein-protein interaction network was constructed and analyzed using 63 Nrf2 regulating candidate genes obtained from NCBI database based on literature studies usingSTRING 10.0database andCytoscape v 3.6.0software plug-inNetwork Analyzer.Further, the functional enrichment analysis of identified gene was done usingPANTHER GENE ONTOLOGYsoftware and DAVID tool.Results:Based on network topological parameter, TP53, JUN, MYC, NFE2L2, AKT1, PIK3CA &amp; UBC were identified as the key gene in the network. Among them, TP53 gene was obtained as a super hub gene with the highest Betweenness Centrality (BC) and node degree. The functional enrichment analysis was done usingPANTHER GENE ONTOLOGYsoftware and DAVID tool reveals their significant role in neurotrophin signaling pathway, MAPK signaling pathway, cellular response to stress &amp; in the regulation of stress.Conclusion:The network analysis will help in prioritizing genes in the pathway that helps in understanding the underlying mechanism of disease. Thus, further study on these genes and their biological mechanism and pathway may, therefore, provide a potential target for the treatment of stress-mediated neurodegeneration.

Network abnormalities in generalized anxiety pervade beyond the amygdala-pre-frontal cortex circuit: Insights from graph theory

Generalized anxiety disorder (GAD) has excessive anxiety and uncontrollable worry as core symptoms. Abnormal cerebral functioning underpins the expression and perhaps pathogenesis of GAD:. Studies implicate impaired communication between the amygdala and the pre-frontal cortex (PFC). Our aim was to longitudinally investigate whether such network abnormalities are spatially restricted to this circuit or if the integrity of functional brain networks is globally disrupted in GAD. We acquired resting-state functional magnetic resonance imaging data from 16 GAD patients and 16 matched controls at baseline and after 1 year. Using network modeling and graph-theory, whole-brain connectivity was characterized from local and global perspectives. Overall lower global efficiency, indicating sub-optimal brain-wide organization and integration, was present in patients with GAD compared to controls. The amygdala and midline cortices showed higher betweenness centrality, reflecting functional dominance of these brain structures. Third, lower betweenness centrality and lower degree emerged for PFC, suggesting weakened inhibitory control. Overall, network organization showed impairments consistent with neurobiological models of GAD (involving amygdala, PFC, and cingulate cortex) and further pointed to an involvement of temporal regions. Such impairments tended to progress over time and predict anxiety symptoms. A graph-analytic approach represents a powerful approach to deepen our understanding of GAD.

Development of the functional social network index for adolescent and young adult cancer survivors

The Brief Coping Orientation to Problem Experience (brief COPE) questionnaire was designed to explore the coping strategies used by individuals in response to stress. It has been used to research various populations with chronic health conditions (1). ‘Betweenness centrality’ is a measure of centrality in a graph based on shortest paths. In a communications network, a node with higher betweenness centrality would have more control over the network, because more information will pass through that node (2). This study compared those instruments with a new index, FSNI to analyse social network in AYA CSs. It is well known that these patients need careful physical and psychological support to maintain a satisfactory quality of life. Accurate identification of “at risk subjects” is necessary to improve social relationships, in particular for those patients (e.g. lymphoma and chemotherapy treated) who showed a higher FSNI. As underlined by the authors, it would be interesting to evaluate relationship characteristics, in particular in terms of age of friends/relatives, to better identify the degree of dependence on them. It would also be interesting to extend the analysis to CSs with onset of cancer in early childhood, for whom the risk of persistent dependence on parents and relatives is high and could interfere with the process of achieving independence during adolescence.

Modeling Team Cognition in Emergency Response via Naturalistic Observation of Team Interactions

Emergency responders work collectively as an ad hoc team to save lives and infrastructures at risk, despite their varying experience, knowledge, cultural backgrounds, and difficult working conditions with high-levels of uncertainty and timepressure. Cognition, in particular, has gained attention as a key construct to consider in collective response efforts in emergency management. Team cognition, however, has not been fully appreciated or adequately addressed in the field of emergency response (Bigley &amp; Roberts, 2001). The interactionist perspective (or interactive team cognition) effectively captures team cognition in heterogeneous and dynamic teams prevalent in the real-world (Cooke &amp; Gorman, 2009; Cooke, Gorman, Myers, &amp; Duran, 2013). Although researchers in the emergency response discipline appreciate the value of viewing team cognition as interaction (Comfort, 2007; Bergeron &amp; Cooren, 2012; Wolbers &amp; Boersma, 2013), an associated empirical or interventional attempt using this perspective remains scarce. Tracing the scarcity of literature back to lack of context-specific theorizing efforts (Moon, Peres, &amp; Sasangohar, 2017), an observation-based, theory-building approach is utilized here to address this gap. The naturalistic observational study presented here is an initial effort to explore team cognition for an incident management team (IMT) as an interactive system. An IMT is an ad hoc team of command-level responders. Interestingly, an IMT is a team of functional sub-teams or sections (i.e., Command, Planning, Operations, Logistics, and Finance/ Administration). Within each sub-team there is also a team of functional units. This naturalistic observational study was conducted at a high-fidelity simulator replicating a generic IMT facility, i.e., the emergency operations training center (EOTC), College Station, TX. Interactions were observed and coded in terms of who initiated the interaction and with whom, which technology was being used, and what was communicated and for what purpose. The purpose of this study is to develop a theoretical interactionist model of team cognition in emergency response, to inform future interventional attempts to improve team decision-making. To do so, this study views a Plans team as a cognitive system capable of managing information through interdependent, nonlinear, and dynamic interactive behaviors for perceiving (P), diagnosing (D), and adapting (A) to the changes in the status of critical elements (Adapted from Moon et al., 2017). The proposed P·D·A model posits the following three premises: (1) a Plans team is a cognitive system where its team cognition is interactions of team members to complete a cognitive task; (2) team cognition for each of the three sub-teams of a Plans team is tied to the context-specific cognitive tasks of perceiving (P), diagnosing (D), and adapting (A) to the changes in the status of critical elements; and (3) team cognition for a Plans team is manifested as nonlinear, interdependent, and dynamic interactions within and among P, D, and A of the three sub-teams of the Plans team. Preliminary results from a content analysis of transcribed and coded interactions suggest that an Info/Intel unit, a Situation unit, and a Section Chief unit can be hypothesized to be critical contributors of team cognition for a Plans team in terms of P, D, and A, respectively. These hypotheses can be represented with network centrality measures as follows: Hypothesis 1. An Info/Intel unit has high in-degree and out-degree centrality with non-Plans teams. Hypothesis 2. A Situation unit has high betweenness centrality within a Plans team. Hypothesis 3. A Section Chief unit has high in-degree and out-degree centrality within a Plans team, and high betweenness centrality between the Plans team and non-Plans teams. The proposed P·D·A model illustrates the benefits of viewing team cognition as interaction within and among a team of teams, for context-specific tasks of P, D, and A. Most importantly, the model effectively captures the nonlinear, interdependent, and dynamic nature of team cognition as interaction in a multiteam system, or MTS (Marks, DeChurch, Mathieu, Panzer, &amp; Alonso, 2005; Bienefeld &amp; Grote, 2014), embedded in complex socio-technical systems, STS (Vicente, 2002). As the information processing model views an individual as a cognitive system or a human information processing system (Wickens, 1992), the P·D·A model views a team as a cognitive system capable of managing information. The interactionist perspective on team cognition helps the P·D·A model to realize its potential to extend an individual cognition model to a team level. The interactionist perspective is “compatible with the view of human-machine system as a unitary system” (Cooke &amp; Gorman, 2009, p. 28). In addition to the theoretical and practical implications, this study has methodological implications. Measuring interactive team cognition with network-based metrics (currently in progress) will open a new chapter. The need of incorporating a network perspective into team cognition in emergency response is in line with the literature (Wolbers &amp; Boersma, 2013; Steigenberger, 2016). As a future work, the P·D·A model will be further developed with network and content analysis and validated through interviews with Subject Matter Experts (SMEs) involved in Hurricane Harvey.

Coral connectivity between equatorial eastern Pacific marine protected areas: A biophysical modeling approach.

There are many marine protected areas (MPAs) containing coral reef aggregations in the eastern Pacific region. However, the connectivity of corals between MPAs is still poorly known, especially in the Marine Conservation Corridor of the Eastern Tropical Pacific (MCCETP). Here, we assess the potential connectivity of corals across equatorial eastern Pacific MPAs through a Lagrangian particle-tracking algorithm coupled offline with an ocean-circulation numerical model. Connectivity metrics and graph theory were used to analyze the networks and highlight those MPAs that are critical for maintaining the connectivity of corals across the region. Our results show that the equatorial eastern Pacific MPAs form a relatively well-connected network, at least 40% of coral larvae released per year end up within the boundaries of an MPA. MPAs like Malpelo and Gorgona islands included in the MCCETP were found to be critical for connectivity of corals because of their high betweenness centrality and potential role as stepping-stones between coastal MPAs and offshore MPAs such as the Galapagos Islands. Two pelagic larval duration (PLD) scenarios (40 and 130 days) indicate a quasi-unidirectional larval flow from coastal MPAs toward oceanic MPAs, where the only resilient MPAs (Coiba and Malpelo islands) depend mostly on subsidiary recruitment from MPAs located along the coast of Costa Rica, Panama and Colombia. In the two PLD scenarios, Cocos Island maintains a very low resilience potential. Our results indicate the imperative need to include coastal MPAs in the MCCETP network initiative, since connectivity and resilience of coral reefs in the equatorial eastern Pacific region rely heavily on coastal MPAs.

A Comparative Study of Fungal Community Structure, Diversity and Richness between the Soil and the Phyllosphere of Native Grass Species in a Copper Tailings Dam in Shanxi Province, China

In the study area, mining processes have led to the accumulation of a large amount of ore sand and a tailings dam was established above this artificial overburden. After a long period of restoration, the area was reclaimed by a variety of native vegetation. This study investigated four of these native grass species, namely, Bothriochloa ischaemum, Imperata cylindrica, Elymus dahuricus and Calamagrostis epigejos, having reestablished themselves after the restoration of a copper tailings dam built in 1969 in Shanxi Province, China. We analyzed the fungal community structure in the soil and the phyllosphere of the four native grass species using high-throughput sequencing. Results showed that the soil of the tailings dam was weakly alkaline and copper (Cu) was the most pervasive element present. Ascomycota were the dominant fungal taxa in the soil and the phyllosphere of all four native grass species, for which total soil nitrogen (N) content was an influencing factor. Basidiomycota was positively correlated to cadmium (Cd), which can additionally be used as an indicator of Cd pollution in copper tailings dams. Among the four native grass species, Nectriaceae was the dominant fungal family found exclusively in B. ischaemum; Meruliaceae and Phaeosphaeriaceae were the dominant fungal families of E. dahuricus; Cordycipitaceae and Sporormiaceae were only found in C. epigejos. However, we found no evidence of a dominant fungal family in I. cylindrica. Furthermore, Erythrobasidiales sp., which had the highest betweenness centrality after network analysis, was identified as the key fungal species in all four native grass species.

Enhancing robustness of metro networks using strategic defense

The safety of metro networks has recently attracted great attention. In this study, we investigated how to allocate security resources in a metro network to enhance its robustness through a defense-attack approach. For each defense-attack model, the defense strategy was first applied to protect a certain proportion of influential nodes in the network and then an attack was executed on the protected network. The influential nodes were identified by eight strategies, one of which, the breadth-tree coefficient strategy, is proposed as a new approach. Simulations of five metro networks showed that attacks on nodes with the highest betweenness centrality value are the most effective for disrupting a metro network, while the robustness of the metro network can be enhanced more efficiently with the proposed breadth-tree coefficient strategy. We also found that the robustness of metro networks benefits less from scattered protected metro stations, since some neglected weakly connected nodes emerge among the optimal influencers. The study also provided insights into how topological features influence the robustness of metro networks. It demonstrated that loop lines and smaller transfer stations can provide more options for alternative routes and be less vulnerable to targeted attacks, which makes the metro network more robust in the face of accidents and terrorist attacks. Our study provides useful information for metro network managers to take decisive actions on how to allocate security resources to increase the robustness of their metro network and design a more robust metro system.

Activation of membrane-bound and soluble Toll-like Receptors 5 in Salmo salar depends on the MyD88 signalling pathway

BackgroundTLR5M and TLR5S are Toll-like Receptors (TLRs), expressed in teleost fish, that recognize flagellin as a ligand, in the same way as the TLR5 ortholog in mammals. However, it has not been demonstrated whether the signalling pathway induced by these TLRs depends on MyD88 to generate a pro-inflammatory response in Salmo salar. MethodsA mathematical model was constructed using the CellDesigner software, which represented the signalling pathways of the all TLRs in S. salar. It was used to make predictions which were corroborated experimentally in Salmo salar Head Kidney Leukocytes (HKLs) treated with flagellin and a MyD88 peptide inhibitor. ResultsThe in silico model consisted of 135 species, 221 nodes and 279 bridges; MyD88 was one of the nodes with the highest betweenness centrality. The model simulations predicted that inhibition of MyD88 or TLR5S would cause a delay in response to stimulation with flagellin. The stimulation of HKLs with flagellin demonstrated a kinetic of relative expression of genes concordant with a positive feedback mechanism between TLR5M, MyD88 and TLR5S. Furthermore, MyD88 inhibition induced a significant decrease in the relative expression of pro-inflammatory genes downstream of the TLR5M signalling pathway. Conclusions and general significanceIn S. salar, activation of TLR5M and TLR5S is dependent on MyD88 as an adaptor protein after stimulation with flagellin. A sequential mechanism of activation, amplification and attenuation of the TLR5M/flagellin signalling pathway is proposed for this species. Our mathematical model is a robust predictive tool for generating new hypotheses about TLRs in S. salar.

Social Networks and Entrepreneurship: Evidence from a Historical Episode of Industrialization

English Abstract: This paper explores the relationship between social networks and entrepreneurship by constructing a dynamic social network from archival records. The network corresponds to the elite of a society in transition to modernity, characterized by difficult geographical conditions, market failures, and weak state capacity, as in late 19th- and early 20th-century Antioquia (Colombia). With these data, I estimate how the decision to found industrial firms related to the position of individuals in the social network. I find that individuals more important bridging the network (i.e. with higher betweenness centrality) were more involved in industrial entrepreneurship. However, I do not find individuals with a denser network to be more involved in this type of activity. The rationale of these results is that industrial entrepreneurship was a highly-complex activity that required a wide variety of complementary resources. Networks operated as substitutes of markets in the acquisition of these resources. Thus, individuals with network positions that favored the combination of a broad set of resources had a comparative advantage in industrial entrepreneurship. I run several tests to prove this rationale. Spanish Abstract: Este documento explora la relaci´on entre las redes sociales y el emprendimiento mediante la construcci´on de una red social din´amica a partir de registros de archivo. La red corresponde a la ´elite de una sociedad en transici´on a la modernidad, caracterizada por condiciones geogr´aficas dif´iciles, fallas de mercado y una baja capacidad estatal, como Antioquia (Colombia) a fines del siglo XIX y principios del XX. Con estos datos, estimo c´omo la decisi´on de fundar firmas industriales se relacion´o con la posici´on de los individuos en la red social. Encuentro que los individuos m´as importantes conectando la red (es decir, con un mayor betweenness centrality)est˜Ain m´as m´as involucrados en el emprendimiento industrial. Sin embargo, no encuentro evidencia de que individuos con una red m´as densa est´en m´as involucrados en este tipo de actividad. La l´ogica de estos resultados es que el emprendimiento industrial era una actividad altamente compleja que requer´ia una amplia variedad de recursos complementarios. Las redes operaban como sustitutos de los mercados en la adquisici´on de estos recursos. Por lo tanto, las personas con posiciones en la red que favorec´ia la combinaci´on de un amplio conjunto de recursos ten´ian una ventaja comparativa en el emprendimiento industrial. Realizo varias pruebas para probar este razonamiento.

Important institutions of interinstitutional scientific collaboration networks in materials science

Interinstitutional scientific collaboration plays an important role in knowledge production and scientific development. Together with the increasing scale of scientific collaboration, a few institutions that positively participate in interinstitutional scientific collaboration are important in collaboration networks. However, whether becoming an important institution in collaboration networks could be a contributing factor to research success and how these important institutions collaborate are still indistinct. In this paper, we identified the scientific institutions that possess the highest degree centrality as important institutions of an interinstitutional scientific collaboration network in materials science and examined their collaboration preferences utilizing several network measures. We first visualized the appearance of these important institutions that had the most positive collaborations in the interinstitutional scientific collaboration networks during the period of 2005---2015 and found an obvious scale-free feature in interinstitutional scientific collaboration networks. Then, we measured the advantages of being important in collaboration networks to research performance and found that positive interinstitutional collaborations can always bring both publication advantages and citation advantages. Finally, we identified two collaboration preferences of these important institutions in collaboration networks--one type of important institution represented by the Chinese Academy of Science plays an intermediary role between domestic institutions and foreign institutions with high betweenness centrality and a low clustering coefficient. This type of important institution has better performance in the number of publications. The other type of important institution represented by MIT tends to collaborate with similar institutions that have positive collaborations and possess a larger citation growth rate. Our finding can provide a better understanding of important institutions' collaboration preferences and have significant reference for government policy and institutional collaboration strategies.

An Innovative Approach to Determining High-Risk Nodes in a Complex Urban Rail Transit Station: A Perspective of Promoting Urban Sustainability

Public safety presents high importance in urban sustainable development. Transportation safety is a significant section in public safety. Over the last couple of decades, as a sustainable means of public transportation, urban rail transit presents a rapid development in China. Increasing initiatives and practices have been engaged with views to facilitating people’s travel and intensive utilizing land resources. Echoing this, rail transit stations with multi-floor structure have been built and show structure complexity. Due to this complexity, there is a need to focus on risk management for the stations to guarantee operation safety. Accordingly, this research introduces an innovative approach to identify high-risk nodes in the complex rail transit stations. The high-risk nodes are determined according to two aspects, which are the key nodes of the station and presenting large passenger volumes. Complex network analysis and field investigation were adopted in this study. The Lianglukou rail transit station in Chongqing, China was selected for case study. The research results in this study indicate that (1) in platform floors, stairs/escalators are almost high-risk nodes; (2) columns and metal fences that have been determined as high-risk nodes are located near stairs/escalators; (3) in concourse floor, the determined high-risk nodes present relative high degree centrality and low betweenness centrality compared with nodes in platform floor. The obtained high-risk nodes are helpful for the management firms to develop risk mitigation measures and re-allocate their resources to create a safe environment for passengers in the stations. The guarantee for the rail transit station operation safety plays an important role in enhancing urban sustainability.

Using core-periphery structure to predict high centrality nodes in time-varying networks

Vertices with high betweenness and closeness centrality represent influential entities in a network. An important problem for time varying networks is to know a-priori, using minimal computation, whether the influential vertices of the current time step will retain their high centrality, in the future time steps, as the network evolves. In this paper, based on empirical evidences from several large real world time varying networks, we discover a certain class of networks where the highly central vertices are part of the innermost core of the network and this property is maintained over time. As a key contribution of this work, we propose novel heuristics to identify these networks in an optimal fashion and also develop a two-step algorithm for predicting high centrality vertices. Consequently, we show for the first time that for such networks, expensive shortest path computations in each time step as the network changes can be completely avoided; instead we can use time series models (e.g., ARIMA as used here) to predict the overlap between the high centrality vertices in the current time step to the ones in the future time steps. Moreover, once the new network is available in time, we can find the high centrality vertices in the top core simply based on their high degree. To measure the effectiveness of our framework, we perform prediction task on a large set of diverse time-varying networks. We obtain F1-scores as high as 0.81 and 0.72 in predicting the top m closeness and betweenness centrality vertices respectively for real networks where the highly central vertices mostly reside in the innermost core. For synthetic networks that conform to this property we achieve F1-scores of 0.94 and 0.92 for closeness and betweenness respectively. We validate our results by showing that the practical effects of our predicted vertices match the effects of the actual high centrality vertices. Finally, we also provide a formal sketch demonstrating why our method works.

Change of influenza pandemics because of climate change: Complex network simulations

IntroductionAirborne influenza virus transmission is depending on climate. Infected individuals are able to travel to any country in the world within one day. In this study we combine these two insights to investigate the influence of climate change on pandemic spreading patterns of airborne infectious diseases, like influenza. Well-known recent examples for pandemics are severe acute respiratory syndrome (SARS, 2002/2003) and H1N1 (Influenza A virus subtype, 2009), which have demonstrated the vulnerability of a strongly connected world.MethodsOur study is based on a complex network approach including the following datasets: –global air traffic data (from openflights.org) with information on airports, direct flight connections, and airplane types; –global population grid [from Socioeconomic Data and Applications Center (SEDAC), NASA]; –WATCH-Forcing-Data-ERA-Interim (WFDEI) climate reanalysis data (1980–2015) and RCP6.0 climate projection data (2016–2040): temperature, specific humidity, surface air pressure, water vapour pressure.We use the dependency between water vapour pressure and influenza transmission rate to give every location around the globe a unique transmission rate time series from 1980 until 2040. Local disease development is simulated with a stochastic SEIR compartmental model. All individuals (including infectious ones) are able to migrate from location to location via air traffic to simulate global dissemination of the virus.ResultsOur results show which regions are most vulnerable to climate change in terms of influenza pandemics towards key target locations (defined by highest degree, highest population, highest betweenness centrality). Furthermore, we point out the influence of climate change on pandemics from 1980 until 2040. A significant trend in the pandemic rate of spreading can be seen on a global scale. Climate change causes an influenza pandemic to proceed 5 days slower (global average) in the year 2040 compared to the year 1980. This trend varies from country to country. For example, pandemics originating from Chad show an accelerated (6 days faster) spread.ConclusionThe presented results focus on the effect that climate change has on spreading patterns of airborne infectious diseases. The change from 1980 until 2040 of important influencing variables like population distribution, varying air traffic, vaccine research, hygiene, and healthcare are neglected to separate the impact of climate change.

Who Spread That Rumor: Finding the Source of Information in Large Online Social Networks With Probabilistically Varying Internode Relationship Strengths

We address the problem of estimating the source of a rumor in large-scale social networks. Previous works studying this problem have mainly focused on graph models with deterministic and homogenous internode relationship strengths. However, internode relationship strengths in real social networks are random. We model this uncertainty by using random, nonhomogenous edge weights on the underlying social network graph. We propose a novel two-stage algorithm that uses the modularity of the social network to locate the source of the rumor with fewer sensor nodes than other existing algorithms. We also propose a novel method to select these sensor nodes. We evaluate our algorithm using a large data set from Twitter and Sina Weibo. Real-world time series data are used to model the uncertainty in social relationship strengths. Simulations show that the proposed algorithm can determine the actual source within two hops, 69%–80% of the time, when the diameter of the networks varies between 7 and 13. Our numerical results also show that it is easier to estimate the source of a rumor when the source has higher betweenness centrality. Finally, we demonstrate that our two-stage algorithm outperforms the alternative algorithm in terms of the accuracy of localizing the source.

Beyond degree and betweenness centrality: Alternative topological measures to predict viral targets.

The availability of large-scale screens of host-virus interaction interfaces enabled the topological analysis of viral protein targets of the host. In particular, host proteins that bind viral proteins are generally hubs and proteins with high betweenness centrality. Recently, other topological measures were introduced that a virus may tap to infect a host cell. Utilizing experimentally determined sets of human protein targets from Herpes, Hepatitis, HIV and Influenza, we pooled molecular interactions between proteins from different pathway databases. Apart from a protein’s degree and betweenness centrality, we considered a protein’s pathway participation, ability to topologically control a network and protein PageRank index. In particular, we found that proteins with increasing values of such measures tend to accumulate viral targets and distinguish viral targets from non-targets. Furthermore, all such topological measures strongly correlate with the occurrence of a given protein in different pathways. Building a random forest classifier that is based on such topological measures, we found that protein PageRank index had the highest impact on the classification of viral (non-)targets while proteins' ability to topologically control an interaction network played the least important role.