Communities Of Different Sizes Research Articles

Alleviating the resolution limit problem in spatial community detection: a local network structure-based method

Spatial community detection plays a crucial role in the analysis of spatially embedded networks. However, most existing methods adopt modularity as an objective function, which may fail to detect small spatial communities (i.e. the well-known resolution-limit problem). To alleviate this problem, a local network structure-based spatially constrained Leiden method was developed. First, the weights of the edges were reset based on the local network structure, which facilitated a clearer delineation between distinct communities. Second, we extended Leiden, an effective community detection method, to spatial community detection content by adding spatial constraints. Experiments on simulated datasets demonstrated that the proposed method is superior to four state-of-the-art methods for detecting spatial communities of different sizes. A case study conducted using the Shenzhen taxi dataset demonstrated that the proposed method outperformed four state-of-the-art methods in revealing urban spatial structures. Notably, the modularity of the spatial communities detected using the proposed method exhibited a marked improvement, nearly doubling that of the four comparative methods in the case study. This study presents a novel and promising framework for detecting spatial communities using modularity.

A global perspective on social stratification in science

To study stratification among scientists, we reconstruct the career-long trajectories of 8.2 million scientists worldwide using 12 bibliometric measures of productivity, geographical mobility, collaboration, and research impact. While most previous studies examined these variables in isolation, we study their relationships using Multiple Correspondence and Cluster Analysis. We group authors according to their bibliometric performance and academic age across six macro fields of science, and analyze co-authorship networks and detect collaboration communities of different sizes. We found a stratified structure in terms of academic age and bibliometric classes, with a small top class and large middle and bottom classes in all collaboration communities. Results are robust to community detection algorithms used and do not depend on authors’ gender. These results imply that increased productivity, impact, and collaboration are driven by a relatively small group that accounts for a large share of academic outputs, i.e., the top class. Mobility indicators are the only exception with bottom classes contributing similar or larger shares. We also show that those at the top succeed by collaborating with various authors from other classes and age groups. Nevertheless, they are benefiting disproportionately from these collaborations which may have implications for persisting stratification in academia.

Epidemicity indices and reproduction numbers from infectious disease data in connected human populations

We focus on distinctive data-driven measures of the fate of ongoing epidemics. The relevance of our pursuit is suggested by recent results proving that the short-term temporal evolution of infection spread is described by an epidemicity index related to the maximum instantaneous growth rate of new infections, echoing concepts and tools developed to study the reactivity of ecosystems. Suitable epidemicity indices can showcase the dynamics of infections, together with commonly employed effective reproduction numbers, especially when the latter assume values less than 1. In particular, epidemicity evaluates the short-term reactivity to perturbations of a disease-free equilibrium. Here, we show that sufficient epidemicity thresholds to prevent transient epidemic outbreaks in a spatially connected setting can be estimated by generalizing existing analogues derived when spatial effects are neglected. We specifically account for the discrete nature, in both space and time, of surveillance data of the type typically employed to estimate effective reproduction numbers that formed the bulk of the communication of the state of the COVID-19 pandemic and its controls. After analyzing the effects of spatial heterogeneity on the considered prognostic indicators, we perform a short- and long-term analysis on the COVID-19 pandemic in Italy, showing that endemic conditions were maintained throughout the duration of our simulation despite stringent control measures. Our method provides a portfolio of prognostic indices that are essential to pinpoint the ongoing pandemic in both a qualitative and quantitative manner, as our results demonstrate. We base our conclusions on extended investigations of the effects of spatial fragmentation of communities of different sizes owing to connectivity by human mobility and contact scenarios, within real geographic contexts and synthetic setups designed to test our framework.

The Imperial Court and the Localities during the Reign of Holy Roman Emperor Friedrich III

Abstract The centrality of the territorial principalities and imperial diet is a defining feature of the historiography of the late medieval Holy Roman Empire. This tendency risks restricting discussions of the Reich’s political life to the arena of high politics and obscuring important links and relationships between the imperial monarchy and its non-princely subjects. This article sets out to examine interactions between the late medieval imperial government and its subjects in the localities. The analysis focuses on the court and government of Emperor Friedrich III of Habsburg (r. 1440–1493), whose ‘peripheral kingship’ has been interpreted by historians as the moment of the crown’s definitive alienation from the Empire’s core lands. The article presents two case studies of protracted legal disputes in which communities of different size, importance and location came into the orbit of the emperor’s court seeking to protect their rights and interests. These case studies provide illustrative examples of how routine matters and local conflicts could be inextricably bound up in the structures of the emperor’s government. This evidence suggests that a complete account of the political and governmental history of the late medieval Holy Roman Empire needs to incorporate both new insights about the effectiveness of the imperial monarchy and the complex, multi-layered and interdependent nature of local politics.

A multiresolution framework for the analysis of community structure in international trade networks

International trade networks are complex systems that consist of overlapping multiple trade blocs of varying sizes. However, the resulting structures of community detection in trade networks often fail to accurately represent the complexity of international trade. To address this issue, we propose a multiresolution framework that integrates information from a range of resolutions to consider trade communities of different sizes and reveal the hierarchical structure of trade networks and their constituent blocks. In addition, we introduce a measure called multiresolution membership inconsistency for each country, which demonstrates the positive correlation between a country’s structural inconsistency in terms of network topology and its vulnerability to external intervention in terms of economic and security functioning. Our findings show that network science-based approaches can effectively capture the complex interdependencies between countries and provide new metrics for evaluating the characteristics and behaviors of countries in both economic and political contexts.

Management of SARS-CoV-2 Omicron Variant Community Screenings in Shanghai, China: A Cross-Sectional Study.

Community screening for SARS-CoV-2 Omicron variant plays a significant role in controlling the spread of infection. However, loopholes may exist in the current management of community screening in Shanghai, China. The objective of this study was to discover loopholes in the management of community screening for SARS-CoV-2 Omicron variant in Shanghai, China and provide targeted solutions. The cross-sectional study was carried out April 4 to April 30, 2021, among residential committee directors from the Putuo District, Pudong District, and Minhang District of Shanghai, China. Data were collected using a self-designed questionnaire about the management of nucleic acid testing (NAT) sampling in communities through the network platform powered by www.wjx.cn. A total of 203 residential committee directors responded to the survey. Of them, 47.3% were not accepted training and 40.4% were not aware of cross-infection. Comparison among sampling sites and communities, high-risk group contained lower proportion of community training (P = 0.093~0.200), higher awareness of cross-infection (P = 0.039~0.777), more medicalworkers (P = 0.007~0.724) and more tests performed (P = 0.001~0.992). Larger communities had more medicalworkers, sampling sites, sampling tables (P = 0.000) and higher awareness of cross-infection (P = 0.009), but lower proportion of community training (P = 0.051). Overall, community training and awareness of infection control were inadequate. Government or institutions should organize the community training and raise the awareness of infection control. Significant differences exist in NAT management patterns between sampling sites, as well as communities of different sizes. Residential community directors minimize high-risk sampling point settings in the future. Special personnel designated by the government or institutions should tour to guide each sampling site.

Shared PV Systems in Multi-Scaled Communities

In past years, Sweden has been facing a rapid growth of photovoltaic cells, and the total PV installation capacity increased from 300 kW to 1090 MW (2006–2020). The increased number of PV users was a result of active support from the Swedish government with an aim of achieving multiple sustainable goals regarding renewable energy. This project evaluates the profitability of shared PV systems in communities of different sizes in Sweden. This study aimed to contribute to the literature by filling the research gap of presenting the financial benefits at different community scales. The electricity use profiles consisted of hourly measured electricity use that was derived from 1067 individual Swedish apartments. The profiles were then used to create multi-scaled communities with shared PV systems. The mid-market price model was implemented to simulate electricity trading among prosumers in the community using Visual Basic Applications (VBA) in MS Excel. Further, the electricity costs were used for Life Cycle Cost (LCC) assessment. To demonstrate the increase in profitability, the LCC results of households with shared PV systems were compared to households that own PV individually and households that do not own a PV system. The evaluation showed the financial benefits of shared PV systems in comparison with individually owned PV systems. This study also demonstrated the increase in profitability and the reduction in payback time for the average household if sharing a PV system as part of a larger community.

Modelling of a multi-stage energy management control routine for energy demand forecasting, flexibility, and optimization of smart communities using a Recurrent Neural Network

This paper proposes an innovative algorithm for community energy management control, able to involve customers in energy trading by exploiting their potential energy flexibility. The main innovation relies on a matrix-based control system where the strategy considers individual and community priorities simultaneously. Through the individual energy flexibility and the community energy pool, the aggregated network energy supply is controlled and shaped. The presented model presents a generalized structure based on control volumes, and it can be universally applied to energy communities of different sizes, number of participants, energy carriers, penetration of photovoltaics, and electric vehicles. The predictive system is conceived from a recurrent neural network, which performs a real-time prediction on energy demands in buildings. Suitable energy flows optimization is also presented with different implications for economic and energy savings. Finally, to show the potential of the developed model, a suitable case study analysis is presented. Important results include the achievement of a typical win-win condition, where both the distribution system operator and final customers benefit from this strategy. Specifically, a reduction of energy demand during demand response events of about 21% is achieved, whereas the interaction with the electricity network decreases of about 15%.

Effects of homophily and heterophily on preferred-degree networks: mean-field analysis and overwhelming transition

We investigate the long-time properties of a dynamic, out-of-equilibrium network of individuals holding one of two opinions in a population consisting of two communities of different sizes. Here, while the agents’ opinions are fixed, they have a preferred degree which leads them to endlessly create and delete links. Our evolving network is shaped by homophily/heterophily, a form of social interaction by which individuals tend to establish links with others having similar/dissimilar opinions. Using Monte Carlo simulations and a detailed mean-field analysis, we investigate how the sizes of the communities and the degree of homophily/heterophily affect the network structure. In particular, we show that when the network is subject to enough heterophily, an ‘overwhelming transition’ occurs: individuals of the smaller community are overwhelmed by links from the larger group, and their mean degree greatly exceeds the preferred degree. This and related phenomena are characterized by the network’s total and joint degree distributions, as well as the fraction of links across both communities and that of agents having fewer edges than the preferred degree. We use our mean-field theory to discuss the network’s polarization when the group sizes and level of homophily vary.

How does homophily shape the topology of a dynamic network?

We consider a dynamic network of individuals that may hold one of two different opinions in a two-party society. As a dynamical model, agents can endlessly create and delete links to satisfy a preferred degree, and the network is shaped by homophily, a form of social interaction. Characterized by the parameter J∈[-1,1], the latter plays a role similar to Ising spins: agents create links to others of the same opinion with probability (1+J)/2 and delete them with probability (1-J)/2. Using Monte Carlo simulations and mean-field theory, we focus on the network structure in the steady state. We study the effects of J on degree distributions and the fraction of cross-party links. While the extreme cases of homophily or heterophily (J=±1) are easily understood to result in complete polarization or anti-polarization, intermediate values of J lead to interesting features of the network. Our model exhibits the intriguing feature of an "overwhelming transition" occurring when communities of different sizes are subject to sufficient heterophily: agents of the minority group are oversubscribed and their average degree greatly exceeds that of the majority group. In addition, we introduce an original measure of polarization which displays distinct advantages over the commonly used average edge homogeneity.

Co‐occurrence patterns at four spatial scales implicate reproductive processes in shaping community assembly in clovers

Abstract Competition, niche differences and chance all contribute to community assembly; yet, the role of reproductive interactions between species is often less appreciated. Closely related plant species that share floral form, phenology and habitat often interact through pollination. They potentially facilitate pollinator attraction, compete for pollination services and/or exchange pollen. If reproductive processes are important to co‐occurrence, we predicted that fitness costs of heterospecific pollen transfer or pollen limitation should result in lower rates of co‐occurrence among outcrossing congeners. In contrast, selfers, which may be less exposed to heterospecific pollen, and/or less negatively affected by it, should co‐occur more frequently. Flower size is an excellent proxy for mating system in clovers. Using herbarium records and three independent field datasets, we documented co‐occurrence patterns of Trifolium at 1 m2–1 km2 scales in California. Using a randomization procedure to reshuffle matrices of community membership, we generated null hypotheses for the expected composition of large‐ and small‐flowered species in Trifolium communities of different sizes. Across all spatial scales, large‐flowered outcrossers were over‐represented at sites lacking congeners, but under‐represented in communities with multiple congeners. Conversely, small‐flowered selfers often occupied sites with multiple other Trifolium species. Patterns for plant height and leaf size, which are weakly or strongly correlated with flower size, did not explain co‐occurrence patterns as robustly. Regression analysis and model selection corroborated the null model analyses, indicating that the likelihood of co‐occurrence decreased as flower size, and thus reliance on outcrossing, increased. Synthesis. This study suggests that reproductive traits and processes may be significant contributors to community assembly and co‐occurrence in flowering plants.

The speakers of minority languages are more multilingual

Aims and objectives/purpose/research questions: The paper tests the hypothesis that the larger the population of language speakers, the smaller the number of second languages mastered by these speakers. Design/methodology/approach: We match the size of the population of 29 Dagestanian languages and the number of second languages spoken by the speakers of these languages from 54 villages, and run a Poisson mixed effects regression model that predicts the average number of second languages spoken by speakers from first-language communities of different size. Data and analysis: Data for this study comes from two sources. The information on the population of Dagestanian languages is based on the digitalized census of 1926. The information on the number of second languages in which the residents of Dagestan are proficient is taken from the database on multilingualism in Dagestan (4032 people). Findings/conclusions: The study supports the hypothesis that the size of language population is negatively correlated with the multilingualism of the language community. Originality: The paper is the first to test the correlation between the size of language population and the level of multilingualism of its speakers using statistical methods and a large body of empirical data. Significance and implications: Population size is a factor that could have influenced patterns of language evolution. The population is interrelated with other factors, one of which is long-standing multilingualism. The methodological lesson of this research is that there is a difference in the level of multilingualism within a range of populations where the largest was about 120,000 people. Limitations: The data is limited to one multilingual region. The revealed correlation probably does not hold for areas where language communities do not interact with their neighbors and even speakers of minority languages can be monolingual, or for the territories where many people migrated and the area where a language is spoken was discontinuous.

Impact of Solidago canadensis L. on Species Diversity of Plant Communities at Different Spatial Scale

It is known that invasive dominants can significantly limit the species diversity of plant communities in microplots. However, it remains unclear whether they are capable of influencing the species diversity of large areas of vegetation. We considered this issue by the example of six areas of plant communities dominated by Solidago сanadensis located in habitats of different types (Belaya River valley, Western Caucasus, 190–210 m above sea level). Within each of them, biomass samples were taken from 25 plots of 0.25 m2, which were then disassembled by species and weighed. The results of the data analysis showed that (1) S. сanadensis significantly reduces the number of accompanying species in microplots of plant communities; however, the process of displacement of these species is predominantly nonselective (random); (2) areas of communities of different sizes with a high and low degree of dominance of S. сanadensis, containing a similar total stock of biomass of accompanying species, include, on average, a similar number of such species; and (3) the species similarity of plant communities located in habitats of different types does not depend on the degree of dominance of S. сanadensis in them. It was concluded that the size of the species pools of cenoses with high and low degrees of dominance of S. сanadensis is approximately the same. Accordingly, an increase in the participation of this species in communities can directly threaten other species only if the area of these communities is relatively small.

СОЦІАЛЬНІ ЕФЕКТИ ПЛАТФОРМЕННОЇ ВЗАЄМОДІЇ ПРИРОДНО-РЕСУРСНОГО ТА РИНКОВОГО ЦИКЛІВ РОЗВИТКУ

The paper identifies key aspects of social changes occurring as the result of strengthening the relations between natural resources’ use and markets’ functioning in the digital economy framework. The traditional model of the market’s interaction with the system of natural resources use is transformed, and the new socio-economic place is established. Main indicators of the process are changes in GDP and employment in various sectors of the economy, modification of the knowledge and information sector into the important factor of production and economic growth, and emergence of the competitiveness. It is concluded the economy’s digitalization is able to ensure more efficient use of natural resources and consequently provide more revenues – primarily due to better assessment and accounting of natural resources and their conservation. However, the digitalization can occur only if special investment and innovation policy, which presumes integration of the science and industry, development of an internal market of high technologies and application of financial instruments. Factors powering the process of integration of the system of natural resources use into the zone of market influence are substantiated. Social effects of the platform interaction of the nature management system and the market for the population, public authorities and the government have been discovered. The maximum effect of their implementation into the processes of market interaction with natural resources should be preceded by structural transformation of the economy, shifting approaches to management of relevant innovations, imperfection of the nature management model and adaptation of the education system. In order to ensure the country's economic development through integration of natural resources into the market framework the differentiated approach is needed to separate policies for local communities of different size and degrees of urbanization, since they have unlike human, financial and technological capacities to digitize local economies. The most effective tools for integrating the system of natural resources use into the market area on the basis of digital technologies have been proposed.

Analytical models for β‐diversity and the power‐law scaling of β‐deviation

Abstract β‐diversity is a primary biodiversity pattern for inferring community assembly. A randomized null model that generates a standardized β‐deviation has been widely used for this purpose. However, the null model has been much debated and its application is limited to abundance data. Here we derive analytical models for β‐diversity to address the debate, clarify the interpretation and extend the application to occurrence data. The analytical analyses show unambiguously that the standardized β‐deviation is a quantification of the effect size of non‐random spatial distribution of species on β‐diversity for a given species abundance distribution. It robustly scales with sampling effort following a power law with exponent of 0.5. This scaling relationship offers a simple method for comparing β‐diversity of communities of different sizes. Assuming log‐series distribution for the metacommunity species abundance distribution, our model allows for calculation of the standardized β‐deviation using occurrence data plus a datum on the total abundance. Our theoretical model justifies and generalizes the use of the β null model for inferring community assembly rules.

Гражданско-патриотическая идентичность студенческой молодежи

Described are some of the key parameters of civil-patriotic identity of student youth. The empirical basis of the study was data of questionnaire survey of students, studying at higher educational institutions of Tyumen on a full-time basis. The survey was conducted by questionnaire method (n = 594), the sample was formed through spontaneous selection of respondents. Three groups of young people were identified, depending on their patriotic self-identification, i.e. “patriots”, “non-patriots” and “vacillating”. Patriotic aspect of self-identification correlates with civic and other forms of social identity, as well as some elements of civic subjectivity. Comparative analysis of selected groups was carried out, according to different indicators of civic orientations and identifications, in particular, subjective significance of citizenship, need to contribute to well-being of the country, degree of interest in history of Russia, emotional connection with symbols of the country, active civic attitudes. The analysis of empirical data also made it possible to identify statistical relationship between self-identification as patriots and communities of different sizes. The conclusion is made about intersection of patriotic and civil identity in terms of civil obligations, responsibility, as well as implementation of symbolic civil actions. In contrast, attitudes associated with critical citizenship are weakly associated with identification of modern students as patriots.

Typology of phase transitions in Bayesian inference problems

Many inference problems undergo phase transitions as a function of the signal-to-noise ratio, a prominent example of this phenomenon being found in the stochastic block model (SBM) that generates a random graph with a hidden community structure. Some of these phase transitions affect the information-theoretic optimal (but possibly computationally expensive) estimation procedure, others concern the behavior of efficient iterative algorithms. A computational gap opens when the phase transitions for these two aspects do not coincide, leading to a hard phase in which optimal inference is computationally challenging. In this paper we refine this description in two ways. From a qualitative perspective, we emphasize the existence of more generic phase diagrams with a hybrid-hard phase, in which it is computationally easy to reach a nontrivial inference accuracy but computationally hard to match the information-theoretic optimal one. We support this discussion by quantitative expansions of the functional cavity equations that describe inference problems on sparse graphs, around their trivial solution. These expansions shed light on the existence of hybrid-hard phases, for a large class of planted constraint satisfaction problems, and on the question of the tightness of the Kesten-Stigum (KS) bound for the associated tree reconstruction problem. Our results show that the instability of the trivial fixed point is not generic evidence for the Bayes optimality of the message-passing algorithms. We clarify in particular the status of the symmetric SBM with four communities and of the tree reconstruction of the associated Potts model: In the assortative (ferromagnetic) case the KS bound is always tight, whereas in the disassortative (antiferromagnetic) case we exhibit an explicit criterion involving the degree distribution that separates a large-degree regime where the KS bound is tight and a low-degree regime where it is not. We also investigate the SBM with two communities of different sizes, also known as the asymmetric Ising model, and describe quantitatively its computational gap as a function of its asymmetry, as well as a version of the SBM with two groups of q_{1} and q_{2} communities. We complement this study with numerical simulations of the belief propagation iterative algorithm, confirming that its behavior on large samples is well described by the cavity method.

Parameterization of biodiversity–productivity relationship and its scale dependency using georeferenced tree‐level data

Abstract The biodiversity–productivity relationship (BPR) constitutes one of the most fundamental yet challenging topics in ecology. Most described BPRs so far are based on relatively small grain sizes (typically smaller than 1ha), and understanding how the BPRs scale up from individual trees to communities of different sizes, a mission critical for biodiversity conservation and forest management, still remains elusive. Based on a unique 54.12‐ha large‐scale forest observational site in Northeastern China, where we georeferenced and measured approx. 90,000 trees in two consecutive inventories, we studied BPRs across a cascade of spatial scales using geostatistical analysis and bootstrapping. To explore the underlying mechanisms of the scale dependency of BPR, we further investigated the scale gradient in biodiversity and productivity, respectively, and assessed neighbourhood influences on individual trees. Across all the spatial scales, we found a consistent positive concave‐down effect of tree species richness on forest productivity. The elasticity of substitution (θ) represented the degree to which species can substitute for each other in contributing to forest productivity. At the community level, the curvature of BPRs in terms of the elasticity of substitution (θ) declined from around 0.50 and converged at around 0.15, as the grain size increased from 0.01 to 1.00 ha, all else being equal. Similarly, at the individual tree level, neighbourhood diversity coefficient, a linear proxy of θ, declined from around 0.08 and converged at around 0.03, as neighbourhood size increased from 0.01 to 1.00 ha, all else being equal. Synthesis. Our study found a pronounced scale dependency of biodiversity–productivity relationship and quantified, for the first time, how biodiversity–productivity relationship scale up from 0.01 to 1.00 ha. Our findings suggest that biodiversity–ecosystem functioning relationship can be generally scale‐dependent and, hence, one must factor in this effect in biodiversity conservation and ecological restoration projects. Our findings also indicate that biodiversity–productivity relationship, predominantly determined by biological processes (i.e. complementarity and diminishing marginal productivity) at a small scale, can be increasingly influenced by statistical (i.e. spatial central limit theorem) and other (such as species–area relationship) processes as the scale increases.

Tie-formation process within the communities of the Japanese production network: application of an exponential random graph model

This paper studies the driving forces behind the formation of ties within the major communities in the Japanese nationwide network of production, which contains one million firms and five million links between suppliers (“upstream" firms) and customers (“downstream" firms). We apply the Infomap algorithm to reveal the hierarchical structure of the production network. At the second level of the hierarchy, we find a reasonable community resolution, where the community size distribution follows a power law decay. Then, we estimate the tie formation within 100 communities of different sizes. The studied model considers a large set of attributes, including both endogenous attributes (network motifs, e.g., stars and triangles) and exogenous attributes (economic variables, e.g., net sales and firm size). The estimation results show that the considered model converges and presents a high goodness of fit (GoF) for all communities. Moreover, it is found that the forces explaining the formation of links between suppliers and customers differ among communities. Some attributes, such as reciprocity, popularity, activity, location homophily, bank homophily and sales statistics, are common drivers of internal link formation for most of the studied communities. However, transitivity is rejected as a significant influencing factor for most communities, reflecting an absence of a sense of trust and reliability between firms with a common partner. Finally, we show that sector homophily does not serve as an obvious mechanism of partnership at the community level in the production network.

Structure and evolution of Indian physics co-authorship networks

We trace the evolution of Indian physics community from 1919 to 2013 by analyzing the co-authorship network constructed from papers published by authors in India in American Physical Society (APS) journals. We make inferences on India’s contribution to different branches of Physics and identify the most influential Indian physicists at different time periods. The relative contribution of India to global physics publication (research) and its variation across subfields of physics is assessed. We extract the changing collaboration pattern of authors between Indian physicists through various network measures. We study the evolution of Indian physics communities and trace the mean life and stationarity of communities by size in different APS journals. We map the transition of authors between communities of different sizes from 1970 to 2013, capturing their birth, growth, merger and collapse. We find that Indian–Foreign collaborations are increasing at a faster pace compared to the Indian–Indian. We observe that the degree distribution of Indian collaboration networks follows the power law, with distinct patterns between Physical Review A, B and E, and high energy physics journals Physical Review C and D, and Physical Review Letters. In almost every measure, we observe strong structural differences between low-energy and high-energy physics journals.