Network Centrality Research Articles

Analyst network centrality, forecast accuracy, and persistent influence

ABSTRACT This paper explores how analysts’ forecasting behaviour is related to their centrality within a dynamic information network. In this network, analysts who issued coverage reports on the same listed firms in clusters are connected. The social learning hypothesis and social capital theory suggest that financial analysts could learn from other analyst forecasts and obtain information from analyst reports. Employing a dynamic complex network methodology, we focus on analysts’ network centrality – degree, betweenness, and closeness – to represent their information access based on a sample of 819,539 analyst forecasts in the Chinese A-share market from 2018 to 2022. Our findings suggest that analysts with more central positions in the network produce more accurate earnings-per-share forecasts and have a longer persistent influence on other analysts. Our results support the perspective that the diffusion of information among analysts affects their forecasts and reporting behaviour.

Matchmakers or chaperones? How venture capital investors facilitate startups’ corporate venture capital partnership formation through aligning and disciplining

Building upon the recent extension of resource dependence theory, this study examines how venture capital (VC) investors provide social defenses for startups by playing the role of matchmakers through the trust-based aligning effect and the role of chaperones through the power-based disciplining effect, encouraging them to form ties with established corporate venture capital (CVC) investors. We argue that the aligning effect stems from VC investors’ prior ties with CVC investors and is mitigated by the disciplining effect from their network centrality and coalition formation likelihood. In our empirical analysis of the IT industry in the United States between 2001 and 2015, we find support for our theory and hypotheses. The results also show that network centrality and coalition formation are not complementary but substitutable as coalition formation is less effective for more central VC investors.

Neural mechanisms of altruistic decision-making: EEG functional connectivity network analysis.

Altruism is an enigmatic form of prosocial behavior, characterized by diverse motivations and significantinterindividual differences. Studying neural mechanisms of altruism is crucial to identify objective markers of pro- and antisocial tendencies in behavior. This study was designed to delve into the mechanisms of altruism by analyzing EEG-based functional connectivity patterns within the framework of the network approach. To experimentally induce a situation of altruistic decision-making, we employed the Pain versus Gain (PvsG) task, which implies making choices concerning financial self-benefit and pain of the other. Our results reveal that the behavioral measure of altruism in the experiment correlated with emotional empathy, which is in line with the "empathy-altruism" hypothesis. Applying the network approach to EEG functional connectivity analysis, we discovered that the very process of decision-making in the PvsG is characterized by the synchronous activity of structures in the right hemisphere, which are involved in empathy for pain. The prosociality of decisions was reflected in functional connectivity between the rostral ACC and orbital IFG in the left hemisphere and the overall network centrality of the caudal ACC. This finding additionally points to the distinct functional roles of the ACC subregions in altruistic decision-making. The proposed neural mechanisms of altruism can furtherbe used to identify neurophysiological markers of prosociality in behavior.

Simulation of policies to promote pro‐environmental behavior in a dynamic learning model of social networks: Cases of Brazil, the United States, Japan, Germany, China, and India

AbstractPro‐environmental behavior does not seem to diffuse sufficiently in Brazil, China, Germany, Japan, India, or the United States. Based on the current distribution of carbon footprints and learning patterns (DeGroot learning, best practice, and free riding) in social networks in the six countries, what will happen to the diffusion of pro‐environmental behaviors in each country? What policy measures will be most effective? This study aims to simulate the diffusion of pro‐environmental behavior and to test the effectiveness of various policy measures in a dynamic social network model in the six countries. The distribution of pro‐environmental behaviors is generated by global gridded carbon footprint data. The distribution of learning patterns in social networks is estimated in each country, using data from the World Values Survey. This study considers three policy measures: increasing the number of people pursuing best pro‐environmental behavior practices (best practice promotion, [BPP]), decreasing the number of free riders (free‐riding reduction, [FRR]), and converting the learning patterns of people with high degrees of centrality into best practice (centrality‐based best practice promotion, [CBPP]). Interesting findings were threefold. First, there are large differences in the estimated degrees of individual pro‐environmental behavior and the proportions of learning patterns in the six countries. Second, FRR and BPP are effective in promoting pro‐environmental behavior, but the choice of FRR or BPP depends on the initial proportion of free riders. Third, CBPP is the most effective policy of the three in all countries. Centrality in social networks must be considered when implementing intervention.

The genetic architecture of repeated local adaptation to climate in distantly related plants.

Closely related species often use the same genes to adapt to similar environments. However, we know little about why such genes possess increased adaptive potential and whether this is conserved across deeper evolutionary lineages. Adaptation to climate presents a natural laboratory to test these ideas, as even distantly related species must contend with similar stresses. Here, we re-analyse genomic data from thousands of individuals from 25 plant species as diverged as lodgepole pine and Arabidopsis (~300 Myr). We test for genetic repeatability based on within-species associations between allele frequencies in genes and variation in 21 climate variables. Our results demonstrate significant statistical evidence for genetic repeatability across deep time that is not expected under randomness, identifying a suite of 108 gene families (orthogroups) and gene functions that repeatedly drive local adaptation to climate. This set includes many orthogroups with well-known functions in abiotic stress response. Using gene co-expression networks to quantify pleiotropy, we find that orthogroups with stronger evidence for repeatability exhibit greater network centrality and broader expression across tissues (higher pleiotropy), contrary to the 'cost of complexity' theory. These gene families may be important in helping wild and crop species cope with future climate change, representing important candidates for future study.

Supply chain network, digital transformation, and investment efficiency

ABSTRACT In the modern digital era, firms’ position in the supply chain networks significantly impacts their investment decision-making. As businesses undergo digital transformation to enhance collaboration and supply chain efficiency, a research gap exists in understanding their combined influence on investment efficiency. Using Chinese public firms from 2008 to 2021 as a sample, we construct a supply chain network and analyse how supply chain network structures affect investment efficiency. Our results show that both supply chain network centrality and structural holes enhance firms’ investment efficiency. Mechanism analysis shows that corporate digital transformation moderates this relationship by mitigating financing constraints and agency costs. In addition, further results reveal that the effect of supply chain networks is stronger for non-state-owned firms, firms in low-marketization region, and firms in closer geographic proximity to their customer base. This study contributes to understanding the roles of supply chain networks and digital transformation in firms’ investment efficiency.

Network centrality, learning capacity and firm performance in equity alliance portfolios

Network centrality, learning capacity and firm performance in equity alliance portfolios

Hub disruption in HIV disease and cocaine use: A connectomics analysis of brain function

BackgroundCocaine use (CU) is prevalent in people with HIV (PWH). Both conditions are linked to changes in cognitive functioning and neural network topology. The current study utilizes graph theory to investigate functional connectomics associated with HIV and CU, focusing on disruption of densely connected nodes called hubs. MethodsResting state functional magnetic resonance imaging (fMRI) from 206 adults (ages 22–55 years) were analyzed. A HIV x CU factorial design was implemented with participants in four groups: HIV+CU (n= 41), HIV only (n= 88), CU only (n= 36), and controls (n= 41). Functional connectomes were constructed, and thresholded graph metrics were calculated. Network centrality metrics – betweenness centrality (BC), participation coefficient (PC), and within module degree (WD) – were quantified into hub disruption indices (HDI). For each index, a 2×2 ANCOVA was performed controlling for education. ResultsParticipants were 68 % male and 74 % African-American with a mean age of 44.4 years. HIV and CU were associated with hub disruption in all three indices. Interactions were significant for HDI-PC and HDI-WD, such that HIV disease was associated with greater hub disruption among participants without CU, but not among participants with CU. Overall, lower global cognitive functioning was associated with greater hub disruption on all three indices. ConclusionsWidespread hub disruption was evident in HIV disease and CU, highlighting topological reorganization in both diseases with neurocognitive effects. Hub-related measures inform functional connectivity disruptions in HIV disease and CU, particularly with respect to changes in network topology throughout the connectome.

Viral Targets in the Human Interactome with Comprehensive Centrality Analysis: SARS-CoV-2, a Case Study

Network centrality analyses have proven to be successful in identifying important nodes in diverse host–pathogen interactomes. The current study presents a comprehensive investigation of the human interactome and SARS-CoV-2 host targets. We first constructed a comprehensive human interactome by compiling experimentally validated protein–protein interactions (PPIs) from eight distinct sources. Additionally, we compiled a comprehensive list of 1449 SARS-CoV-2 host proteins and analyzed their interactions within the human interactome, which identified enriched biological processes and pathways. Seven diverse topological features were employed to reveal the enrichment of the SARS-CoV-2 targets in the human interactome, with closeness centrality emerging as the most effective metric. Furthermore, a novel approach called CentralityCosDist was employed to predict SARS-CoV-2 targets, which proved to be effective in expanding the pool of predicted targets. Pathway enrichment analyses further elucidated the functional roles and potential mechanisms associated with predicted targets. Overall, this study provides valuable insights into the complex interplay between SARS-CoV-2 and the host’s cellular machinery, contributing to a deeper understanding of viral infection and immune response modulation.

Co‐Jump Dependency and Transmission Across US Commodity Futures: A Network Analysis

ABSTRACTThis paper examines the co‐jump transmission in 20 commodity futures returns in the United States using co‐jump network models. Specifically, it reveals co‐jumping behavior in both static and time‐varying settings, considering the overall commodity markets and various commodity groups separately, which helps us understand the dynamic changes in co‐jump dependencies at the overall and sector levels. The main results reveal that co‐jump heterogeneity exists among commodities but is generally more apparent within each commodity group, and co‐jumps vary over time. Gold exerts the strongest influence, with many commodity futures being influenced by the jumps behavior in gold returns. During the COVID‐19 outbreak and the Russia–Ukraine war, the energy group ranks highest in terms of co‐jump network centrality. Our empirical analysis highlights the portfolio performance and risk reduction, and an additional examination shows that centrality information from the co‐jump network contains a highly and statistically forecasting power for US stock market volatility.

Bilateral tax treaties network and global value chain position

This study investigates the effect of bilateral tax treaty (BTT) network centrality on countries’ global value chain (GVC) positions using a panel dataset of 62 economies from 2007 to 2021. Employing an instrumental variable approach, we find that higher BTT network centrality reduces a country's overall GVC position, with heterogeneous effects across sectors and participation types (forward and backward). The results suggest that increased BTT network centrality may facilitate production fragmentation and enable tax planning strategies (treaty shopping), leading to a distortion in the distribution of value-added across countries in GVCs.

Network centrality and firm performance: A meta-analysis

Network centrality and firm performance: A meta-analysis

The effect of family physician team processes on chronic disease management performance from a structural network perspective

ObjectiveThe family physician team has become the core carrier for delivery primary health care in China. This study aimed to measure the effect of the network structural characteristics of family physician team processes on health performance. Strategic recommendations for optimizing the family physician team processes with a view to improving performance were presented.MethodsA cross-sectional survey was conducted from October to December 2021 in Qianjiang in Hubei Province and Changsha in Hunan Province. Task performance, contextual performance, social networks, and sociodemographic characteristics were collected. Social network analysis was conducted to calculate density and centralization, then hierarchical linear regression analysis was employed to explore the relationship between the network structural characteristics of family physician team processes and performance.ResultsIn total, 88 family physician teams attended in this investigation. The transition processes of family physician team showed a distinctive low density (0.272 ± 0.112), high centralization (0.866 ± 0.197) network structure. For family physician team, the density of action processes significantly and positively affected task performance (B = 0.600, P < 0.05); the centralization of action processes positively affected task performance (B = 0.604, P < 0.01); the density of action processes positively affected contextual performance (B = 0.545, P < 0.01); the density of interpersonal processes significantly and positively affected contextual performance (B = 0.326, P < 0.05).ConclusionThe network density and centralization of family physician team processes have positive effects on chronic disease management performance. The results from this study help to enhance our conceptual understanding of social network and its implications for team-dynamics. Optimizing family physician team processes is an effective way to strengthen the construction of family physician team and promote the quality and efficiency of family physician-contracted service. It is recommended to strengthen the management of team processes, enhance the internal collaboration mechanism, and optimize the centralized network structure of family physician team.

Channel diversity, constructive and destructive conflicts: the moderating role of network centrality

PurposeThis study aims to investigate the impact of channel diversity on constructive and destructive conflicts in manufacturer-distributor relationships, as well as the mediating role of distributor opportunism and the moderating role of distributor network centrality.Design/methodology/approachThis paper collected channel dual data to represent channel behavior in manufacturer-distributor relationships accurately. Using 208 matched manufacturer distributor bilateral data from Chinese B2B firms validate the conceptual model. This paper tested the proposed hypothesis using AMOS 24.0 software.FindingsChannel diversity strengthens constructive conflict and weakens destructive conflict between manufacturers and distributors. Distributor opportunism plays a mediating role between channel diversity and constructive conflict. Distributor network centrality reinforces the positive impact of channel diversity on constructive conflict and the impact of channel diversity on destructive conflict.Originality/valueThis research contributes significantly to developing channel diversity and conflict theory by expanding the dyadic view to a network view. It also provides valuable insights for managers, highlighting the importance of maintaining a good relationship with distributors with high network centrality. The research results allow the constructive aspects of channel conflict to manifest fully and enhance the effectiveness of channel management. Distributor firms should prioritize communication and contact with network members to increase their network centrality, enabling them to take more initiative, exert influence and establish advantageous cooperative partnerships with manufacturers.

Workaholism, work engagement, and burnout among academics in Montenegro: A psychometric network approach.

The academic environment is known for its high demands in research, teaching, and administration, that along with increasing publish or perish culture can lead to reduced psychological well-being and mental health issues. This study aimed to investigate the associations between workaholism, work engagement, and burnout among academics in Montenegro. A cross-sectional design was used to develop anonymous online survey. Data was collected from 131 participants employed as teaching and research staff at public and private universities. To measure the variables of interest we used: ultra-short Utrecht Work Engagement Scale (UWES-3), the work-related burnout subscale from the Copenhagen Burnout Inventory (CBI-7) and the Dutch Work Addiction Scale (DUWAS-10). Psychometric network analysis was employed to examine the relationships among variables. The findings revealed two distinct clusters: the first containing the dimensions of work engagement and the second containing burnout and the dimensions of workaholism. The two clusters were connected with the dimensions of dedication - burnout having the strongest edge (-0.25 and -0.40). In the cross-sample network the strongest connection was burnout -working excessively (.35). No significant differences in network density (0.80 (12/15 edges)) and global strength (p = 0.159) in the networks of public and private universities were found. Results of the network centrality and the edge strength analyses suggest that the interventions focused at increasing dedication while not fostering a work environment that encourages working excessively might be the key to preventing and reducing burnout in academia across contexts of public and private universities.

Nurse burnout: deep connections and solutions revealed by network analysis

BackgroundBurnout is very important to nurses’ physical and mental health and career development. Current approaches to assessing nurse burnout tend to use a total score modeling paradigm to explore the impact of external factors on burnout. The purpose of this study is to delve into the item-level relationship between nurse burnout and its influencing factors at both the social and psychological levels using a network analysis approach.MethodsThis study was conducted in June 2023 and 1,005 nurses from 4 hospitals out of 8 tertiary care hospitals in Harbin were selected to participate in this study using whole cluster sampling method. Measurements included a general demographic questionnaire, Trait coping styles questionnaire, Organizational commitment questionnaire, Work-family conflict scale, Transformational leadership questionnaire, and Maslach Burnout Inventory-General Survey.ResultsOur results suggest that “C1 Time-Based work interference with family” showed the strongest centrality and bridging in the overall network. This finding underscores its centrality to burnout. Other strongest bridge symptoms included “E2 Reduced personal accomplishment” and “A1 Positive coping styles” indicating their strongest connections to other clusters.ConclusionsNurse administrators should be encouraged to pay more attention to nurses’ work situations and family distress, and to help nurses in a flexible way.

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

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

How Does CEO Succession Affect Innovation Ambidexterity? The Role of Board Network Centrality

How Does CEO Succession Affect Innovation Ambidexterity? The Role of Board Network Centrality

Team Network Centrality and LMX: A Better Way to Differentiate?

Team Network Centrality and LMX: A Better Way to Differentiate?

Harmony under threat: does resource-mediated stress affect the (caste-based) social network of leaf-cutting ant colonies?

Managing pest species of eusocial insects, such as leaf-cutting ants, poses significant challenges. Controlling them requires understanding of how toxic plant substrates and ant baits are recognized by foragers, transported to the nest, shared among workers and managed by gardeners cultivating the symbiont fungus garden. Despite this, little is known about how unsuitable resources might impact social interactions within ant colonies. This study aims to investigate whether the provision of a suitable substrate (copperleaf) and a toxic substrate (nasturtium leaves) affects the social network dynamics within colonies of two leaf-cutting ant species: Acromyrmex molestans and Acromyrmex subterraneus. The interactions between castes were recorded and subjected to social network analyses. Initial foraging duration increased for A. subterraneus provided with copperleaf, although no difference was observed for the other species and resource combinations. The social network structure was similar for both species when copperleaf leaves were provided as a substrate. However, notable alterations occurred with nasturtium leaf provision, leading to higher integration of gardeners in interactions and noticeable changes in the generalist worker network centrality, particularly in A. subterraneus. The observed changes in social interactions, particularly in A. subterraneus, suggest that increasing gardener interactions with other castes expedites the movement of the substrate within the colony. This maximizes the potential toxic effect on the colony. © 2024 Society of Chemical Industry.