Network Centrality Research Articles

The detection of self–group conflicts in exercise behaviors differs with social network centrality: ERP evidence

BackgroundThe influence of social norms on exercise behaviors has been explored in studies over the years. However, little is known about whether an individual’s role (central or peripheral) in his or her social network, which is associated with social skills, could shift his or her susceptibility to normative effects on exercise behaviors. To that end, event-related potentials (ERPs) were recorded to examine the underlying cognitive mechanism of the effects of network centrality on normative social influence. MethodsWe manipulated network centrality by assigning participants to exercise support groups, with group members who were their nominated friends (high centrality) or nonnominated classmates (low centrality). Participants were asked to evaluate their willingness to engage in various exercises, after viewing discrepant group ratings (peer influence) or not viewing (no-influence). ResultsPeer influence evoked a larger negative-going feedback-related negativity (FRN) wave, which was linked to automatic social conflict detection, and a larger positive-going P3 wave, which was linked to subsequent conformity behavioral changes. However, effects on the FRN, not the P3, were observed only in the high-centrality group. ConclusionOur results highlight the important roles of network centrality in encoding self–group exercise attitude discrepancy rather than in decision-making regarding exercise attitude adjustments. Interventions aimed at promoting exercise behaviors should be considered in a broader social environmental framework.

Knowledge Networks, Collaboration Networks, and Local Search Behaviors

Inventors embed themselves in knowledge networks consisting of links between knowledge elements and collaboration networks with other inventors. These two networks are decoupled: Their structural features are distinct, and they jointly influence inventors’ local searches for knowledge. Using a longitudinal patent dataset comprising 12,174 observations, this study investigates the effects of two structural features of inventors in these networks—degree centrality and structural holes—on their local search behaviors. Our findings reveal that the degree centrality of inventors’ knowledge elements in the intra-organizational knowledge network contributes to the depth and breadth of their local search behaviors, while structural holes hinder such behaviors. Furthermore, the effect of degree centrality in the intra-organizational knowledge network for local searches increases when inventors hold central positions and span more structural holes in the intra-organizational collaboration network. The negative effect of structural holes in the knowledge network for local searches weakens when inventors hold central positions in the collaboration network. These findings contribute to the literature on knowledge searches by decoupling the knowledge and collaboration networks and analyzing their effects on the depth and breadth of local searches and offer practical implications to organizational managers and researchers.

Game Theoretic Foundations of the Gately Power Measure for Directed Networks

We introduce a new network centrality measure founded on the Gately value for cooperative games with transferable utilities. A directed network is interpreted as representing control or authority relations between players—constituting a hierarchical network. The power distribution embedded within a hierarchical network can be represented through appropriate TU-games. We investigate the properties of these TU-representations and investigate the Gately value of the TU-representation resulting in the Gately power measure. We establish when the Gately measure is a core power gauge, investigate the relationship of the Gately with the β-measure, and construct an axiomatisation of the Gately measure.

Well-being is associated with cortical thickness network topology of human brain

BackgroundLiving a happy and meaningful life is an eternal topic in positive psychology, which is crucial for individuals’ physical and mental health as well as social functioning. Well-being can be subdivided into pleasure attainment related hedonic well-being or emotional well-being, and self-actualization related eudaimonic well-being or psychological well-being plus social well-being. Previous studies have mostly focused on human brain morphological and functional mechanisms underlying different dimensions of well-being, but no study explored brain network mechanisms of well-being, especially in terms of topological properties of human brain morphological similarity network.MethodsTherefore, in the study, we collected 65 datasets including magnetic resonance imaging (MRI) and well-being data, and constructed human brain morphological network based on morphological distribution similarity of cortical thickness to explore the correlations between topological properties including network efficiency and centrality and different dimensions of well-being.ResultsWe found emotional well-being was negatively correlated with betweenness centrality in the visual network but positively correlated with eigenvector centrality in the precentral sulcus, while the total score of well-being was positively correlated with local efficiency in the posterior cingulate cortex of cortical thickness network.ConclusionsOur findings demonstrated that different dimensions of well-being corresponded to different cortical hierarchies: hedonic well-being was involved in more preliminary cognitive processing stages including perceptual and attentional information processing, while hedonic and eudaimonic well-being might share common morphological similarity network mechanisms in the subsequent advanced cognitive processing stages.

Senolytic treatment reduces oxidative protein stress in an aging male murine model of post-traumatic osteoarthritis.

Senolytic drugs are designed to selectively clear senescent cells (SnCs) that accumulate with injury or aging. In a mouse model of osteoarthritis (OA), senolysis yields a pro-regenerative response, but the therapeutic benefit is reduced in aged mice. Increased oxidative stress is a hallmark of advanced age. Therefore, here we investigate whether senolytic treatment differentially affects joint oxidative load in young and aged animals. We find that senolysis by a p53/MDM2 interaction inhibitor, UBX0101, reduces protein oxidative modification in the aged arthritic knee joint. Mass spectrometry coupled with protein interaction network analysis and biophysical stability prediction of extracted joint proteins revealed divergent responses to senolysis between young and aged animals, broadly suggesting that knee regeneration and cellular stress programs are contrarily poised to respond as a function of age. These opposing responses include differing signatures of protein-by-protein oxidative modification and abundance change, disparate quantitative trends in modified protein network centrality, and contrasting patterns of oxidation-induced folding free energy perturbation between young and old. We develop a composite sensitivity score to identify specific key proteins in the proteomes of aged osteoarthritic joints, thereby nominating prospective therapeutic targets to complement senolytics.

Exploring the association between network centralities and passenger flows in metro systems

Network science offers valuable tools for planning and managing public transportation systems, with measures such as network centralities proposed as complementary predictors of ridership. This paper explores the relationship between different cases of passenger flows at metro stations and network centralities within both metro and alternative public transport (substitute) networks; such an association can be useful for managing metro system operations when disruptions occur. For that purpose, linear regression and non-parametric machine learning models are developed and compared. The Athens metro system is used as a testbed for developing the proposed methodology. The findings of this study can be used for deriving medium-term ridership estimates in cases of metro disruptions, as the proposed methodology can support contingency plans for both platform and rail track disruptions.

Information Transmission in a Social Network: A Field Experiment

AbstractUsing an app for smartphones, we run an experiment among high-school students to study the pattern of aggregation of sparsely distributed information. Agents are randomly arranged in small networks and can share only non-verifiable pieces of information. Results show that while information exchange is high, the level and the distribution of centralities among network members are important to shape the overall level of information aggregation. A reduction in the asymmetry among agents’ network centralities is associated with an improvement in the performance of the group in terms of aggregation of information.

How rediscovering nodality can improve democratic governance in a digital world

AbstractIn Hood's (1983) classic “NATO” scheme, nodality—centrality and visibility in information networks—is one of the four tools of government. Despite its potential as a resource to effect policy change, contemporary scholars have largely neglected this unique policy tool. However, the growing prominence of digital platforms in public life prompts a rediscovery of this concept. The decisive shift is the parallel growth of citizen nodality, which can increase societal capacity and enhance government nodality, partly by allowing citizens to challenge government nodality. Moreover, citizen nodality and government nodality can interact to promote more democratic government. With the aim of rediscovering and updating nodality, this article defines the concept, reviews how the modern digital landscape has transformed the practice of nodality, and proposes a conceptual and methodological toolkit. The goal is to understand how nodality may be more effectively used by policy‐makers while at the same time fostering democratic governance.

Does supply chain network centrality affect firm cost stickiness?

This study constructs a supply chain network for Chinese listed firms from 2009 to 2021 and investigates the influence of the firm’s supply chain network position on cost stickiness. Our findings reveal that the supply chain network centrality exacerbates cost stickiness by increasing adjustment costs and agency problems, this effect applies to firms in both industrial and services sectors, and remains valid whether before and after the COVID-19 pandemic. Furthermore, high-quality internal controls and external audits can mitigate the effect of supply chain network centrality on cost stickiness. This study enriches the determinants of cost stickiness literature by providing evidence at the supply chain network level.

Impact of the belt and road initiative on trade status and FDI attraction: A local and global network perspective

The Belt and Road Initiative (BRI) is attracting an increasing number of countries to join it for financial support and development opportunities. However, have the countries had higher trade status and been more attractive to foreign direct investment since joining the BRI? Have they cooperated more intensively with other countries? To answer the questions, based on two mixed network centrality measures, we utilize a difference-in-differences approach to examine the impact of the BRI on trade status and FDI attraction using a sample of 93 countries during 2008–2018 from a local and global network view. In multi-dimension characteristic and dynamic evolution analysis, we capture distinct network changes after the BRI. Our empirical results indicate a positive BRI effect on trade status and FDI attraction of BRI countries. From a local network view, we find the BRI effect on trade status requires a longer period, while the BRI effect on FDI attraction fades over time. We document a whole-period positive BRI effect on trade status and FDI attraction from a global network view. Moreover, the pioneering role and mediating effect of FDI attraction are identified. Last, we also find that the BRI alleviates the trade deficit and domestic capital flight.

Decentralized self-stabilizing primary control of microgrids

The usage of distributed generation sources and the segmentation of power systems into microgrids brings several advantageous features, such as energy independence, lower transmission losses, and improved energy management flexibility. Still, the expected steady abolition of traditional centralized networks and their centralized control schemes introduces several operational difficulties, that can compromise (micro)grid’s stability and reliability. Consequently, several approaches to how microgrids should be operated have been proposed, albeit with several salient deficiencies. Precise power exchange, control of frequency and voltage excursions, applicability on different grid topologies, and the possibility to integrate grid codes, which are very important control aspects, are not jointly enabled in a general case. This paper proposes a novel approach, based on a specific goal function and corresponding decentralized control that addresses the mentioned problems and guarantees the microgrid’s stability by constraining the frequency and node voltage deviations, simultaneously supporting the desired active power exchange between prosumers. Unlike droop-based and droop-derived control schemes, the proposed approach aims to inherently preserve the frequency and voltage levels within the limits defined by the grid operator or future microgrid codes, thus preventing the generation unit’s unwanted disconnection during transients and disturbances. In addition, the control algorithm is independent of network topology, can be applied in both islanded and non-islanded microgrids, and secures system scalability. To confirm the effectiveness of the proposed control strategy, a number of simulation results are presented and thoroughly discussed.

Venture capital firms' lead orientation and entry to new investment markets

This study focuses on how a venture capital (VC) firm's lead orientation (i.e., the propensity to act as lead investors rather than follower investors in its past syndication investments) influences its decision to enter new investment markets, and how such a relationship is altered by VC firm's position in the syndication network. Based on empirical analyses that use detailed information on 11,219 investment deals in China from January 1999 to June 2016, we find an inverted U-shaped relationship between VC firms' lead orientation and the distance between the new-entered market and the existing markets. In addition, a VC firm's network centrality weakens the inverted U-shaped relationship, while the level of network constraint strengthens the main effect. To establish our findings' reliability, we conducted robustness checks using different measures and subsamples. Additionally, we employed the instrumental variable approach and the Heckman selection model to address potential endogeneity issues, further strengthening the validity of our findings.

The influence of social network on innovation and entrepreneurship of economic and management college students based on adaptive particle swarm algorithm

Abstract Exploring the influence of social networks on innovation and entrepreneurship of economics and management college students to help them better enhance their innovation consciousness to face the future social workplace. In this paper, the particle swarm algorithm is optimized according to the strategy and parameter adaptive mechanism, and the SPS-PSO algorithm is used to initialize and update the population. The social network and innovation and entrepreneurship ability of university students in economics and management are introduced, and feature selection based on strategy and parameter adaptive particle swarm algorithm is utilized. The index mining of social network and innovation and entrepreneurship ability of college students is conducted, and the data analysis of innovation and entrepreneurship ability of college students in economics and management is carried out with the example of the University of S. From the social network, the percentages of those who agree with the validity of social network centrality, heterogeneity, dynamics and linkage strength are 71.56, 71.69%, 72.55% and 71.16%, respectively. From the perspective of innovation and entrepreneurship, the overall percentage of college students in economics and management who agreed was 69.27%. This indicates that the higher the integrity of the social network structure of economics and management college students, the more it can enhance innovation and entrepreneurship abilities and promote the cultivation of observation and judgment ability and divergent thinking abilities of economics and management college students.

Spreading expertise: think tanks as digital advocators in the social media era

Abstract Think tanks and their roles in rendering expertise in policy advice have been the subject of historical and ongoing research in policy sciences. However, the widening trend of digital behaviors of think tanks in the social media era is yet to be investigated. While social media is reshaping the political ecology and challenging conventional sources of knowledge, it also serves as a potential channel for think tanks to publicly share their expertise and policy advice with the public and decision-makers, thus evolving into “digital advocators.” Based on social media big data methods, this paper has constructed two sets of new think tank indicators with “network centrality” of social media accounts and “ripple effect” of social media citations from Facebook and Twitter to capture and observe the digital activities of 207 prominent think tanks in 62 countries or regions. We have concluded that think tanks have displayed their opinion and values and increased visibility as digital advocators by spreading expertise on social media platforms with online marketing skills. With various values and ideas, social media not only is hosting massive policy advocacy activities and being an expertise spreading arena for think tanks but has also been changing the ecology of the think tank industry and expertise markets.

The Second Cold War: US-China Competition for Centrality in Infrastructure, Digital, Production, and Finance Networks

ABSTRACT Relations between the US and China have deteriorated to their lowest point since their rapprochement in the 1970s. To make sense of contemporary geopolitics, our objective in this article is two-fold. First, we historically situate contemporary US-China rivalry to conceptualise the Second Cold War (SCW). We argue that in the wake of the 2008 financial crisis, both the US and China launched ‘restorative’ political projects that harked back to imagined pasts. These projects are mutually exclusive and animate contemporary geopolitics. Second, we conceptualise the spatial logic of great power rivalry in the Second Cold War. In contrast to the first Cold War, when great powers sought to incorporate territory into blocs, the US and China currently compete on a global scale for centrality in four interrelated networks that they anticipate will underpin hegemony in the 21st century: infrastructure (e.g. logistics and energy), digital, production and finance. We review the state of competition in each network and draw two broad conclusions: (1) this mode of competition makes it difficult for either side to conclusively ‘win’ the Second Cold War, and (2) many countries are likely to remain integrated with both the US and China.

The effects of longitudinal fragmentation on riverine beta diversity are modulated by fragmentation intensity

The loss of longitudinal connectivity affects river systems globally, being one of the leading causes of the freshwater biodiversity crisis. Barriers alter the dispersal of aquatic organisms and limit the exchange of species between local communities, disrupting metacommunity dynamics. However, the interplay between connectivity losses due to dams and other drivers of metacommunity structure, such as the configuration of the river network, needs to be explored. In this paper, we analyzed the response of fish communities to the network position and the fragmentation induced by dams while controlling for human pressures and environmental gradients. We studied three large European catchments covering a fragmentation gradient: Upper Danube (Austrian section), Ebro (Spain), and Odra/Oder (Poland). We quantified fragmentation through reach-scaled connectivity indices that account for the position of barriers along the dendritic network and the dispersal capacity of the organisms. We used generalized linear models to explain species richness and Local Contributions to Beta Diversity (LCBD) and multilinear regressions on the distance matrix to describe Beta Diversity and its Replacement and Richness Difference components. Results show that species richness was not affected by fragmentation. Network centrality metrics were relevant drivers of beta diversity for catchments with lower fragmentation (Ebro, Odra), and fragmentation indices were strong beta diversity predictors for the catchment with higher fragmentation (Danube). We conclude that in highly fragmented catchments, the effects of network centrality/isolation on biodiversity could be masked by the effects of dam fragmentation. In such catchments, metapopulation and metacommunity dynamics can be strongly altered by barriers, and the restoration of longitudinal connectivity (i.e. the natural centrality/isolation gradient) is urgent to prevent local extinctions.

Normalized closeness centrality of urban networks: impact of the location of the catchment area and evaluation based on an idealized network

The decision of where to locate the catchment area of an urban network exerts significant influence on the indicator values and in this research this influence is referred to as the placement effect. Placement effect has significant impact on the studies at the neighborhood scale focusing on the structural properties of the network models, the network analysis results and centrality measures, the inferred movement patterns and the accessibility to destination. Placement effect becomes even more significant when multiple catchment areas are sampled to be compared or classified. This research examines placement effect on one of the most affected indicators, closeness centrality, and proposes using an idealized network as a reference to be compared with the real network in order to find a solution to mitigate the placement effects. By comparing the normalized closeness centrality in the real network with that in the idealized network, we can (1) evaluate the placement effect on the closeness centrality and (2) find the threshold distance in order to mitigate the placement effect. The results show that the closeness centrality of the same node varies remarkably depending on its position and how central it is in the chosen catchment area. Specifically, in the selected areas in this research, if the center point of a catchment area is moved by more than 100 m away from the original center point, the closeness centrality of the same node starts to be significantly influenced by the placement effect. The threshold distance of 100 m offers a recommendation that a direct comparison of the closeness centrality between different nodes in the same catchment area should be drawn only if these nodes are less than 100 m away from each other. In other words, when comparing two nodes located further than the threshold distance from each other, it is advisable to create two separate catchment areas, where these nodes serve as the center points. It should be noted that the threshold distance of 100 m derived specifically from the current research should not be generalized to other cases. The threshold distance of different case studies remains open for further investigation in the future as it may vary among cities or areas.

Centrality in the macroeconomic multi-network explains the spatiotemporal distribution of country per-capita income

This paper empirically investigates the role played by cross-country spillovers in shaping spatiotemporal differences in country income. While existing literature focused on effects captured by direct spillovers with partner countries only, here we take a complex network perspective to explore whether the global embeddedness of countries in the macroeconomic multi-network may significantly impact income, net of country local characteristics such as local foreign exposure. We employ data for the period 2000–2020 to build a time sequence of 3-layer multi graphs, with countries as nodes and links weighted by the intensity of bilateral relations in international trade, finance and human migration. Using panel-regression techniques, we then ask if country (eigenvector) centrality in the multi network can account for parts of the observed heterogeneity in country per-capita income, both cross-sectionally and over time. Robustly across a number of alternative specifications of the empirical model, we find that being more central significantly boosts country income. This implies that income-enhancing technological spillovers are not only channeled via local exposure, but also through indirect interactions with more distant nodes.

Some generalized centralities in higher-order networks represented by simplicial complexes

Abstract Higher-order interactions, that is, interactions among the units of group size greater than two, are a fundamental structural feature of a variety of complex systems across the scale. Simplicial complexes are combinatorial objects that can capture and model the higher-order interactions present in a given complex system and thus represent the complex system as a higher-order network comprising simplices. In this work, a given simplicial complex is viewed as a finite union of d-exclusive simplicial complexes. Thus, to represent a complex system as a higher-order network given by a simplicial complex that captures all orders of interactions present in the system, a family of symmetric adjacency tensors A(d) of dimension d + 1 and appropriate order has been used. Each adjacency tensor A(d) represents a d-exclusive simplicial complex and for d≥2 it represents exclusively higher-order interactions of the system. For characterizing the structure of d-exclusive simplicial complexes, the notion of generalized structural centrality indices namely, generalized betweenness centrality and generalized closeness centrality has been established by developing the concepts of generalized walk and generalized distance in the simplicial complex. Generalized centrality indices quantify the contribution of δ-simplices in any d-exclusive simplicial complex Δ, where δ<d and if d≥2, it describes the contribution of δ-faces to the higher-order interactions of Δ. These generalized centrality indices provide local structural descriptions, which lead to mesoscale insights into the simplicial complex that comprises the higher-order network. An important theorem providing a general technique for the characterization of connectedness in d-exclusive simplicial complexes in terms of irreducibility of its adjacency tensor has been established. The concepts developed in this work together with concepts of generalized simplex deletion in d-exclusive simplicial complexes have been illustrated using examples. The effect of deletions on the generalized centralities of the complexes in the examples has been discussed.

Fine-scale collective movements reveal present, past and future dynamics of a multilevel society in Przewalski’s horses

Studying animal societies needs detailed observation of many individuals, but technological advances offer new opportunities in this field. Here, we present a state-of-the-art drone observation of a multilevel herd of Przewalski’s horses, consisting of harems (one-male, multifemale groups). We track, in high spatio-temporal resolution, the movements of 238 individually identified horses on drone videos, and combine movement analyses with demographic data from two decades of population monitoring. Analysis of collective movements reveals how the structure of the herd’s social network is related to kinship and familiarity of individuals. The network centrality of harems is related to their age and how long the harem stallions have kept harems previously. Harems of genetically related stallions are closer to each other in the network, and female exchange is more frequent between closer harems. High movement similarity of females from different harems predicts becoming harem mates in the future. Our results show that only a few minutes of fine-scale movement tracking combined with high throughput data driven analysis can reveal the structure of a society, reconstruct past group dynamics and predict future ones.