Walktrap Algorithm Research Articles

408 Network Analysis of Neurosurgical Publication Coauthorship: Centrality Ranking of Authors and Institutions with Correlation to Clinical Activity and Industry Payments

INTRODUCTION: Network analysis has been applied in a number of fields to understand relationships between members of different communities and organizations. METHODS: All publications from January 1, 2017, to April 30, 2020, were queried from 4 neurosurgical journals: Neurosurgery, Journal of Neurosurgery, Journal of Neurosurgery: Pediatrics, and Journal of Neurosurgery: Spine. Publication type excluded editorials, letters, and reviews. Authors with at least 7 publications in these journals were included. Measures of network centrality were calculated after conversion of authorship into a nondirected graph with links weighted by frequency of coauthorship. Latent communities in the network were detected with the Walktrap algorithm. RESULTS: Based on 811 included authors, network topology broadly demonstrated 4 poles related to subspecialties of vascular, spine, pediatrics, and radiosurgery/tumor. Of the top 100 coauthorship links, 42% of the authors were involved in 2 large study groups. We present rankings of neurosurgical authors based on network measures of centrality and evaluate how centrality metrics correlate to individual author h-index, Medicare utilization, and payments from industry sources. We further evaluate network centrality of institutions and examine institutional connectivity to discovered communities within the network. CONCLUSION: While traditional metrics like h-index demonstrate long-term research involvement, network analysis can be applied to understand current relationships between academic authors and evaluate centrality within a publishing network during a given timeframe.

Depressive symptom complexes of community-dwelling older adults: a latent network model.

Late-life depression has multiple, heterogeneous clinical presentations. The aim of the study was to identify higher-order homogeneous clinical features (symptom complexes), while accounting for their potential causal interactions within the network approach to psychopathology. We analyzed cross-sectional data from community-dwelling adults aged 65-85 years recruited by the European MentDis_ICF65+ study (n = 2623, mean age 74, 49% females). The severity of 33 depressive symptoms was derived from the age-adapted Composite International Diagnostic Interview. Symptom complexes were identified using multiple detection algorithms for symptom networks, and their fit to data was assessed with latent network models (LNMs) in exploratory and confirmatory analyses. Sensitivity analyses included the Partial Correlation Likelihood Test (PCLT) to investigate the data-generating structure. Depressive symptoms were organized by the Walktrap algorithm into eight symptom complexes, namely sadness/hopelessness, anhedonia/lack of energy, anxiety/irritability, self-reproach, disturbed sleep, agitation/increased appetite, concentration/decision making, and thoughts of death. An LNM adequately fit the distribution of individual symptoms' data in the population. The model suggested the presence of reciprocal interactions between the symptom complexes of sadness and anxiety, concentration and self-reproach and between self-reproach and thoughts of death. Results of the PCLT confirmed that symptom complex data were more likely generated by a network, rather than a latent-variable structure. In conclusion, late-life depressive symptoms are organized into eight interacting symptom complexes. Identification of the symptom complexes of late-life depression may streamline clinical assessment, provide targets for personalization of treatment, and aid the search for biomarkers and for predictors of outcomes of late-life depression.

Measuring organizational climate via psychological networks analysis

PurposeThis study investigates organizational climate under the thriving at work perspective using a network approach. The authors demonstrate how organizational climate functions as a complex system and what relationships between variables from different dimensions are the most important to characterize the construct.Design/methodology/approachBy surveying 119,266 workers from 284 companies based in Brazil, the authors estimated a Gaussian graphical model with LASSO regularization for the complete dataset and for two subsets of cases randomly drawn from the whole dataset. The walktrap algorithm was applied for community detection, and a strong model for measurement invariance was fit to test whether the organizational climate is perceived similarly across groups.FindingsResults show that the networks estimated for both groups are quite consistent, with similar number of communities and items detected. The same pattern was found for the expected influence of each item. Measurement invariance was confirmed, showing that organizational climate is perceived similarly in both groups. The most important community detected and whose items have higher levels of centrality was organizational commitment, followed by a community centered around macro-organizational aspects covering cultural integrity, organizational agility and responsible leadership.Research limitations/implicationsStudies in the field have attested to the possibility of investigating the phenomenon from four (Campbell et al., 1970) to over 80 dimensions (Koys and DeCottis, 1991). As a result, since several dimensions have been produced to investigate organizational climate, there is no consensus on the quality and number of dimensions that should be considered to measure such a vast and multifaceted construct. Built on thriving at work perspective, eight dimensions were devised to cover a wide range of characteristics that distinguish organizational climate, including those related to Industry 4.0 (Coetzee, 2019). However, one may argue that a few dimensions, namely social responsibility, diversity and inclusion, or even more items describing work-life balance could expand the depth and breadth of the instrument and potentially trigger new associations that might eventually impose a new logic to the comprehension of climate as a system. Future studies combining the dimensions investigated in this study with other dimensions are therefore highly recommended for an even more comprehensive investigation.Practical implicationsThe results of this investigation show how to apply psychological networks to gain insights into different variables and dimensions of organizational climate. These findings can be used for the development of organizational policies focused on the most relevant aspects of organizational climate. This information would allow organizations to go beyond simply describing the individual frequencies for each item and could even be used to create a weighted scoring model that could prioritize variables with higher levels of centrality.Originality/valueTo the authors’ knowledge, this is the first study that investigates organizational climate using psychological networks; it provides a better understanding of the relationships established between items from different dimensions as opposed to the common cause framework whose focus is on the investigation of dimensions separately.

The item network and domain network of burnout in Chinese nurses

BackgroundAs a common social phenomenon, nurses’ occupational burnout has a high incidence rate, which seriously affects their mental health and nursing level. The current assessment mostly uses the total score model and explores the influence of external factors on burnout, while the correlation between burnout items or dimensions is less explored. Ignoring the correlation between the items or dimensions may result in a limited understanding of nurse occupational burnout. This paper explores the item and dimension network structure of the Maslach Burnout Inventory-General Survey (MBI-GS) in Chinese nurses, so as to gain a deeper understanding of this psychological construct and identify potential targets for clinical intervention.MethodsA total of 493 Chinese nurses were recruited by cluster sampling. All participants were invited to complete the survey on symptoms of burnout. Network analysis was used to investigate the item network of MBI-GS. In addition, community detection was used to explore the communities of MBI-GS, and then network analysis was used to investigate the dimension network of MBI-GS based on the results of community detection. Regularized partial correlation and non-regularized partial correlation were used to describe the association between different nodes of the item network and dimension network, respectively. Expected influence and predictability were used to describe the relative importance and the controllability of nodes in both the item and dimension networks.ResultsIn the item network, most of the strongly correlated edges were in the same dimension of emotional exhaustion (E), cynicism (C) and reduced professional efficacy (R), respectively. E5 (Item 5 of emotional exhaustion, the same below) “I feel burned out from my work”, C1 “I have become more callous toward work since I took this job”, and R3 “In my opinion, I am good at my job” had the highest expected influence (z-scores = 0.99, 0.81 and 0.94, respectively), indicating theirs highest importance in the network. E1 “I feel emotionally drained from my work” and E5 had the highest predictability (E1 = 0.74, E5 = 0.74). It shows that these two nodes can be interpreted by their internal neighbors to the greatest extent and have the highest controllability in the network. The spinglass algorithm and walktrap algorithm obtained exactly the same three communities, which are consistent with the original dimensions of MBI-GS. In the dimension network, the emotional exhaustion dimension was closely related to the cynicism dimension (weight = 0.65).ConclusionsThe network model is a useful tool to study burnout in Chinese nurses. This study explores the item and domain network structure of nurse burnout from the network perspective. By calculating the relevant indicators, we found that E5, C1, and R3 were the most central nodes in the item network and cynicism was the central node in the domain network, suggesting that interventions aimed at E5, C1, R3 and cynicism might decrease the overall burnout level of Chinese nurses to the greatest extent. This study provides potential targets and a new way of thinking for the intervention of nurse burnout, which can be explored and verified in clinical practice.

Bridging Brain and Cognition: A Multilayer Network Analysis of Brain Structural Covariance and General Intelligence in a Developmental Sample of Struggling Learners.

Network analytic methods that are ubiquitous in other areas, such as systems neuroscience, have recently been used to test network theories in psychology, including intelligence research. The network or mutualism theory of intelligence proposes that the statistical associations among cognitive abilities (e.g., specific abilities such as vocabulary or memory) stem from causal relations among them throughout development. In this study, we used network models (specifically LASSO) of cognitive abilities and brain structural covariance (grey and white matter) to simultaneously model brain–behavior relationships essential for general intelligence in a large (behavioral, N = 805; cortical volume, N = 246; fractional anisotropy, N = 165) developmental (ages 5–18) cohort of struggling learners (CALM). We found that mostly positive, small partial correlations pervade our cognitive, neural, and multilayer networks. Moreover, using community detection (Walktrap algorithm) and calculating node centrality (absolute strength and bridge strength), we found convergent evidence that subsets of both cognitive and neural nodes play an intermediary role ‘between’ brain and behavior. We discuss implications and possible avenues for future studies.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches.

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease.Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network. Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches.

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease.Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network. Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target "ERBB4" and candidate drug "Wortmannin" provide insights on the possible personalized therapeutics for emerging COVID-19.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background: Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS. Methods: Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network. Results: Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system. Conclusion: We conclude that CoV drug target “ERBB4” and candidate drug “Wortmannin” provide insights on the possible personalized therapeutics for emerging COVID-19.

Identifying potential drug targets and candidate drugs for COVID-19: biological networks and structural modeling approaches

Background:Coronavirus (CoV) is an emerging human pathogen causing severe acute respiratory syndrome (SARS) around the world. Earlier identification of biomarkers for SARS can facilitate detection and reduce the mortality rate of the disease. Thus, by integrated network analysis and structural modeling approach, we aimed to explore the potential drug targets and the candidate drugs for coronavirus medicated SARS.Methods:Differentially expression (DE) analysis of CoV infected host genes (HGs) expression profiles was conducted by using the Limma. Highly integrated DE-CoV-HGs were selected to construct the protein-protein interaction (PPI) network. Results:Using the Walktrap algorithm highly interconnected modules include module 1 (202 nodes); module 2 (126 nodes) and module 3 (121 nodes) modules were retrieved from the PPI network. MYC, HDAC9, NCOA3, CEBPB, VEGFA, BCL3, SMAD3, SMURF1, KLHL12, CBL, ERBB4, and CRKL were identified as potential drug targets (PDTs), which are highly expressed in the human respiratory system after CoV infection. Functional terms growth factor receptor binding, c-type lectin receptor signaling, interleukin-1 mediated signaling, TAP dependent antigen processing and presentation of peptide antigen via MHC class I, stimulatory T cell receptor signaling, and innate immune response signaling pathways, signal transduction and cytokine immune signaling pathways were enriched in the modules. Protein-protein docking results demonstrated the strong binding affinity (-314.57 kcal/mol) of the ERBB4-3cLpro complex which was selected as a drug target. In addition, molecular dynamics simulations indicated the structural stability and flexibility of the ERBB4-3cLpro complex. Further, Wortmannin was proposed as a candidate drug to ERBB4 to control SARS-CoV-2 pathogenesis through inhibit receptor tyrosine kinase-dependent macropinocytosis, MAPK signaling, and NF-kb singling pathways that regulate host cell entry, replication, and modulation of the host immune system.Conclusion:We conclude that CoV drug target “ERBB4” and candidate drug “Wortmannin” provide insights on the possible personalized therapeutics for emerging COVID-19.

Using network analysis to identify seasonal patterns and key nodes for risk‐based surveillance of pig diseases in Italy

The description of the pattern of livestock movements between herds provides essential information for both improving risk-based surveillance and to understand the likely spread of infectious diseases. This study provides a description of the temporal pattern of pig movements recorded in Italy on a 4-year period (2013-2016). Data, provided by the National Livestock registry, were described by social network analysis and the application of a walk-trap algorithm for community detection. Our results show a highly populated community located in Northern Italy, which is the focal point of the Italian industrial pig production and as a general pattern an overall decline of medium and backyard farms and an increase in the number of large farms, in agreement with the trend observed by other EU pig-producing countries. A seasonal pattern of all the parameters evaluated, including the number of active nodes in both the intensive and smaller production systems, emerged: that is characterized by a higher number of movements in spring and autumn, linked with the breeding and production cycle as pigs moved from the growing to the finishing phase and with periods of increased slaughtering at Christmas and Easter. The same pattern was found when restricting the analysis to imported pig batches. Outbreaks occurring during these periods would have a greater impact on the spread of infectious diseases; therefore, targeted surveillance may be appropriate. Finally, potential super-spreader nodes have been identified and represent 0.47% of the total number of pig holdings (n=477). Those nodes are present during the whole study period with a similar ranking in their potential of being super-spreaders. Most of them were in Northern Italy, but super-spreaders with high mean out-degree centrality were also located in other Regions. Seasonality, communities and super-spreaders should be considered when planning surveillance activity and when applying disease control strategies.

Comparison of Two Network-Theory-Based Methods for detecting Functional Regions

Abstract Background: Functional regions are abstract, uniformly defined territorial units that form an important basis for many development strategies of a country or a region. Objectives: This study analyses the application of network theory to the detection of such regions. Methods/Approach: Functional regions are analysed using two methods based on the graph theory: the Walktrap algorithm and the chain approach. The quality of the two regionalization methods is analysed using the fuzzy set theory with the revised method. Slovenia was used as a case study. Results: The Walktrap algorithm generated eight functional regions; seven of them corresponded to those identified in previous studies. The only difference occurred in the northwestern mountainous part of Slovenia. The chain approach led to similar results, although it resulted in a huge functional urban region of the capital Ljubljana. Conclusions: The results show that the Walktrap algorithm calculates regions that are more closed, where more workers find work in the home region, than the chain approach.

Comparative analysis of sequential community detection algorithms based on internal and external quality measure

Community detection is a dominant research field in social media mining. Numerous algorithms have been developed to detect community structure in network. Each of them focuses on different characteristic of data and generates different communities for same dataset. So cluster validation or comparative analysis of these algorithms is required to compare the efficiency of these algorithms. There are two known ways for cluster validation: Internal Quality Measure (IQM) and External Quality Measure (EQM). IQM evaluates the cluster without reference to external information while EQM requires already known clusters i.e. ground truth for comparison. Most of the researchers have compared Community Detection algorithms on the basis of either IQM or EQM but not both. In this paper, we have tried to compare nine existing sequential community detection algorithms on the basis of both IQM and EQM and we observed that overall performance of Louvain, Fastgreedy and Walktrap algorithm is more efficient than other algorithms. From this paper, researchers working in this area can get information about major sequential community detection algorithms and their efficiency on small real world datasets. They can incorporate the findings to develop more efficient algorithms.

Symptoms of mood disorders in family carers of older people with dementia who experience caregiver burden: a network approach

informal carers of people with dementia are at greater risk of anxiety and depressive disorders if they find caregiving to be a burden. The aim of this study was to use a network analysis of cross-sectional data to investigate the relationships between anxiety and depressive symptoms in family carers of older people with dementia who experience burden. sixty family carers exhibiting high levels of burden using the Zarit Burden Interview were included in the study. Participants completed the Hospital Anxiety and Depression Scale. The network analysis identified the depression and anxiety symptom network using features including a topological graph, network centrality metrics and community analysis. The network was estimated through the graphical LASSO technique in combination with a walktrap algorithm to obtain the clusters within the network and the connections between the nodes (symptoms). A directed acyclic graph was generated to model symptom interactions. the resulting network architecture shows important bridges between depression and anxiety symptoms. Lack of pleasure and loss of enjoyment were identified as potential gateway symptoms to other anxiety and depression symptoms and represent possible therapeutic targets for psychosocial interventions. Fear and loss of optimism were highly central symptoms, indicating their importance as warning signs of more generalised anxiety and depression. this network analysis of depressive and anxiety symptoms in overburdened family carers provides important insights as to what symptoms may be the most important targets for behavioural interventions.

Spatial patterns and determinant factors of population flow networks in China: Analysis on Tencent Location Big Data

Despite the rising interest in understanding the population flows across cities, few empirical studies have paid close attention to their network characteristics and determining factors. Using the Tencent Location Big Data, this paper first analyzes the spatial patterns of interregional population mobility among 328 Chinese cities and aims to identify the influential factors associated with population flows based on the exponential random graph model (ERGM). Supported by social network analysis method, we found that the spatial distribution patterns of population flows were relatively stable, showing a diamond shape with 7 central points, with the most attractive cities located in east developed urban agglomerations. Nevertheless, the proportion of cities with a positive population inflow in the west increased. The subgroup structure gained by Walktrap algorithm suggested that the intra-province flows comprised the majority. Furthermore, variants of ERGMs were used to determine geographic, demographic, economic, natural, and linguistic factors, as well as others related to the level of facilities. The results are in line with the neoclassical economics theory and the amenity-led theory, while amenities' factor played a more significant role. Our research can provide important insights for strengthening network connections among cities to achieve more balanced regional development.

An efficient method to detect communities in social networks using DBSCAN algorithm

The detection of the communities and the depiction of the interactions, between the entities and the individuals in the real world network graphs is a challenging problem. There are many conventional ways to detect those interconnected nodes which lead to the detection of communities. The strength of the detected communities can be detected by its modularity which is a measurement of the structure of a graph, and increasing the modularity is also a bit challenging problem. So, in this work, the DBSCAN clustering algorithm has been implemented for the task of detecting the outliers in the process of detecting the communities in a social network, and those outliers which are also known as “noisy nodes”, are removed from the main formed network graph. The proposed algorithm in this paper, mainly focuses on the detection and removal of those noisy nodes or outliers in the detected communities which leads to the improvement of the quality of the detected communities. In previous community detection algorithms, some algorithms needed the number of communities prior to the formation of communities which precludes from forming a good community, while some algorithms cannot operate with the huge amount of data and some algorithms require a huge amount of memory. The proposed algorithm does not require any prior mentioning of the number of communities, it has also been tested with large networks with a size of more than 1000 nodes and it does not require much space. Therefore, the proposed algorithm has overcome the mentioned limitations of the previous community detection algorithms. The data have been collected from the social network websites-Facebook and Twitter. The communities formed from the proposed algorithm have been compared with the results of the four other community detection algorithms, i.e., with the Louvain algorithm, Walktrap algorithm, Leading eigenvector algorithm, and Fastgreedy algorithm. The proposed methodology performs well for the detection of communities with the increment of the strength of the detected communities.

Evaluation of the impact of live pig trade network, vaccination coverage and socio-economic factors in the classical swine fever eradication program in Peru

Classical swine fever (CSF) is a viral infectious disease of swine with significant economic impact in the affected countries due to the limitation of trade, culling of infected animals and production losses. In Latin America, CSF is endemic in several countries including Ecuador, Bolivia, Brazil and Peru. Since 2010, the National Veterinary Services of Peru have been working to better control and eradicate the disease with an intensive vaccination program. The aim of this study was to evaluate the effectiveness of the vaccination program and determine which factors are still contributing to the persistence of the disease in certain regions of Peru. We integrated the data from the vaccination campaign, the live pig movement network and other socioeconomic indicators into a multilevel logistic regression model to evaluate their association with CSF occurrence at district level. The results revealed that high vaccination coverage significantly reduces the risk of CSF occurrence (OR = 0.07), supporting the effectiveness of the vaccination program. Districts belonging to large and medium pig trade network communities (as identified with walktrap algorithm) had higher probability to CSF occurrence (OR = 2.83 and OR = 5.83, respectively). The human development index (HDI) and the presence of a slaughterhouse in the district was also significantly associated with an increased likelihood of CSF occurrence (OR = 1.52 and OR = 3.25, respectively). Districts receiving a high proportion of the movements from districts that were infected in the previous year were also at higher risk of CSF occurrence (OR = 3.30). These results should be useful to guide the prioritization of vaccination strategies and may help to design other intervention strategies (e.g., target education, movement restrictions, etc.) in high-risk areas to more rapidly advance in the eradication of CSF in Peru.

Network analysis of empathy items from the interpersonal reactivity index in 1973 young adults

The aim of this work is to perform a network analysis on the French adaptation of the interpersonal reactivity index (IRI) scale from a large Belgian database and provide additional information for the construct of empathy. We analyze a database of 1973 healthy young adults who were queried on the IRI scale. A regularized partial correlation network is estimated. In the visualization of the model, items are displayed as nodes, edges represent regularized partial correlations between the nodes. Centrality denotes a node's connectedness with other nodes in the network. The spinglass algorithm and the walktrap algorithm are used to identify communities of items, and state-of-the-art stability analyses are carried out. The spinglass algorithm identifies four communities, the walktrap algorithm five communities. Positive edges are found among nodes belonging to the same community as well as among nodes belonging to different communities. Item 14 (“Other people's misfortunes do not usually disturb me a great deal”) shows the highest strength centrality score. The network edges and node centrality order are accurately estimated. Network analysis highlights interesting connections between indicators of empathy; how these results impact empathy models must be assessed in further studies.

The top 2,000 cited articles in critical care medicine: a bibliometric analysis.

The bibliometric analysis has been performed on several topics in critical care medicine (CCM) focusing on top 100 cited articles, but the analysis on CCM literature as a whole is missing. The present study aimed to perform a complete bibliometric analysis in the field of CCM. An electronic search of the Scopus database was performed on Feb 13, 2018. The search strategy involved core terms related to CCM. The top 2,000 most cited articles in the field of CCM were included in the analysis. Descriptive statistics on these top-cited articles, country distributions, and journals are reported. Individual author's productivity was assessed with the Lotka's law. Co-occurrence of keywords was visualized with the Fruchterman-Reingold layout. The Walktrap algorithm was employed for clustering analysis. A total of 2,000 documents were included in the analysis with median citations of 386 times [interquartile range (IQR): 308-562 times]. The most cited article was the original paper that described the Acute Physiology and Chronic Health Evaluation (APACHE) II score. The included articles were published in 411 journals. The median number of documents published in one journal was 1, and the mean number was 4.9, indicating a skewed distribution. The maximum number of publications was 217 in CCM. Author's productivity profile was significantly different from the Lotka's law (P=0.001), with n and C values of 2.8 and 0.52, respectively. Fruchterman-Reingold network plot showed that studies involving human subject were the most common literature type. Sepsis was a major research topic that co-occurred with keywords such as disease severity, nonhuman, risk assessment and practice guideline. The study performed bibliometric analyses of 2,000 top-cited articles in CCM. The most cited article was the one which developed the APACHE II score. Author's productivity was significantly different from the Lotka's law.

Comparing Community Detection Algorithms in Transport Networks via Points of Interest

Passengers travel in transport networks with diverse interests represented by linked points of interest (POIs) and drive urban regions to group into network communities. Previous studies focused on applying community detection methods (CDMs) to discover spatial mobility patterns or using POIs to explain the decision making of human mobility, without comparing the effectiveness of CDMs for detecting network communities. In this paper, we analyze the relationship between POIs and network communities of human mobility over diverse CDMs. Taking the taxi systems of Shanghai and Beijing as case studies, we construct transport networks with urban regions as nodes and the connections between them as links weighted by mobility flows. The spatial communities are identified based on the movement strength among regions. POIs are mapped into nodes in the network and are considered as independent variables for classifying the spatial community categories. Our study suggests that communities detected with two specific CMDs (namely, the Combo algorithm and the Walktrap algorithm) correlate to POIs, and the correlation of the Combo is the best ( $R^{2}=0.3$ for Shanghai and $R^{2}=0.48$ for Beijing). In this regard, this paper can provide valuable insight into understanding the formation of spatial communities and assist in selecting reasonable CDMs.

Uncovering Spatial Structures of Regional City Networks from Expressway Traffic Flow Data: A Case Study from Jiangsu Province, China

On the basis of the “space of flows” theory, city networks emphasizing factor connectivity and spatial connection have become a core perspective for regional spatial relationships. They provide a context for discussing the spatial structures of city networks and a scientific basis for making regional development policies. Relying on expressway traffic flow data from Jiangsu Province in 2014, this study describes macro-spatial patterns and hierarchical structures of city networks, and uses the Walktrap algorithm of community detection to execute space divisions and elucidate potential spatial connection structures in the city networks. In comparison with other algorithms, the Walktrap algorithm demonstrates significant adaptation and stability to the short expressway traffic flows with the characteristics of strong network density and sparse node distribution. The results indicate that the macro-spatial patterns of Jiangsu’s city networks have clear regional differences. The cities with relatively dense spatial linkages are distributed along the Yangtze River banks, and many different sub-network systems have also developed internally. Cities and linkage axles have clear hierarchical structures. City hierarchies have certain spatial couplings with the cities’ intrinsic social economic attributes and geographical locations. The linkage axles hierarchy has clear spatial interactions and superposition with geographical distance. The community detection algorithm identified six spatially connected “city communities”, the Suzhou-Wuxi-Changzhou Community (SWCC), the Nanjing-Zhenjiang-Yangzhou-Taizhou Community (NZYTC), the Nantong-Yancheng Community (NYC), the Lianyungang Community (LYGC), the Huai’an-Suqian Community (HSC), and the Xuzhou Community (XZC). The community spatial metaphors had four aspects. First, trans-prefectural linkages were formed through spatially integrated effects of metropolis regions. Second, some communities share the same boundaries with their prefecture level administrative units; this reveals that significant administrative regional economies still exist in contemporary Jiangsu. Third, several cities located in the marginal areas of prefectures and captured by the powerful center cities in neighboring prefectures have been absorbed into the communities of neighboring prefectures. Fourth, the two cities Jiangyin and Jingjiang, divided into different administrative districts, have switched status opposite city community divisions.