Strong Community Structure Research Articles

Efficient community-based control strategies in adaptive networks

Most studies on adaptive networks concentrate on the properties of steady state, but neglect transient dynamics. In this study, we pay attention to the emergence of community structure in the transient process and the effects of community-based control strategies on epidemic spreading. First, by normalizing the modularity, we investigate the evolution of community structure during the transient process, and find that a strong community structure is induced by the rewiring mechanism in the early stage of epidemic dynamics, which, remarkably, delays the outbreak of disease. We then study the effects of control strategies started at different stages on the prevalence. Both immunization and quarantine strategies indicate that it is not ‘the earlier, the better’ for the implementation of control measures. And the optimal control effect is obtained if control measures can be efficiently implemented in the period of a strong community structure. For the immunization strategy, immunizing the susceptible nodes on susceptible–infected links and immunizing susceptible nodes randomly have similar control effects. However, for the quarantine strategy, quarantining the infected nodes on susceptible–infected links can yield a far better result than quarantining infected nodes randomly. More significantly, the community-based quarantine strategy performs better than the community-based immunization strategy. This study may shed new light on the forecast and the prevention of epidemics among humans.

Opinion formation in time-varying social networks: The case of the naming game

We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.

The Influence of Life History Milestones and Association Networks on Crop-Raiding Behavior in Male African Elephants

Factors that influence learning and the spread of behavior in wild animal populations are important for understanding species responses to changing environments and for species conservation. In populations of wildlife species that come into conflict with humans by raiding cultivated crops, simple models of exposure of individual animals to crops do not entirely explain the prevalence of crop raiding behavior. We investigated the influence of life history milestones using age and association patterns on the probability of being a crop raider among wild free ranging male African elephants; we focused on males because female elephants are not known to raid crops in our study population. We examined several features of an elephant association network; network density, community structure and association based on age similarity since they are known to influence the spread of behaviors in a population. We found that older males were more likely to be raiders than younger males, that males were more likely to be raiders when their closest associates were also raiders, and that males were more likely to be raiders when their second closest associates were raiders older than them. The male association network had sparse associations, a tendency for individuals similar in age and raiding status to associate, and a strong community structure. However, raiders were randomly distributed between communities. These features of the elephant association network may limit the spread of raiding behavior and likely determine the prevalence of raiding behavior in elephant populations. Our results suggest that social learning has a major influence on the acquisition of raiding behavior in younger males whereas life history factors are important drivers of raiding behavior in older males. Further, both life-history and network patterns may influence the acquisition and spread of complex behaviors in animal populations and provide insight on managing human-wildlife conflict.

The potential for targeted surveillance of live fish movements in Scotland

The network structure of the movements of live fish in the Scottish aquaculture industry has recently been demonstrated for 2003. In this paper, we enlarge this analysis to a longer 3-year period from 2002 to 2004, the new data allowing complete coverage of at least one production cycle. The resulting network contains slightly more sites than that for a single year and is denser with more arcs (directed site-to-site connections) present, but otherwise features recognizable in the 1-year network are still recognizable in the 3-year network. Arc-removal algorithms (a proxy for targeted surveillance) were identified that could successfully reduce the portion of the network reachable from a node (a proxy for potential epidemic size) by approximately one-third by removing as few as four arcs. This results from the high centrality of particular nodes and arcs. A strong community structure was identified in the network, corresponding with species farmed, but only weakly geographical, with a high proportion of arcs occurring between management areas and catchments.

Variability of contact process in complex networks.

We study numerically how the structures of distinct networks influence the epidemic dynamics in contact process. We first find that the variability difference between homogeneous and heterogeneous networks is very narrow, although the heterogeneous structures can induce the lighter prevalence. Contrary to non-community networks, strong community structures can cause the secondary outbreak of prevalence and two peaks of variability appeared. Especially in the local community, the extraordinarily large variability in early stage of the outbreak makes the prediction of epidemic spreading hard. Importantly, the bridgeness plays a significant role in the predictability, meaning the further distance of the initial seed to the bridgeness, the less accurate the predictability is. Also, we investigate the effect of different disease reaction mechanisms on variability, and find that the different reaction mechanisms will result in the distinct variabilities at the end of epidemic spreading.

Indications of marine bioinvasion from network theory

The transport of huge amounts of small aquatic organisms in the ballast tanks and at the hull of large cargo ships leads to ever increasing rates of marine bioinvasion. In this study, we apply a network theoretic approach to examine the introduction of invasive species into new ports by global shipping. This is the first stage of the invasion process where it is still possible to intervene with regulating measures. We compile a selection of widely used and newly developed network properties and apply these to analyse the structure and spread characteristics of the directed and weighted global cargo ship network (GCSN). Our results reveal that the GCSN is highly efficient, shows small world characteristics and is positive assortative, indicating that quick spread of invasive organisms between ports is likely. The GCSN shows strong community structure and contains two large communities, the Atlantic and Pacific trading groups. Ports that appear as connector hubs and are of high centralities are the Suez and Panama Canal, Singapore and Shanghai. Furthermore, from robustness analyses and the network’s percolation behaviour, we evaluate differences of onboard and in-port ballast water treatment, set them into context with previous studies and advise bioinvasion management strategies.

Interplay between structure and dynamics in adaptive complex networks: Emergence and amplification of modularity by adaptive dynamics

Many real networks display modular organization, which can influence dynamical clustering on the networks. Therefore, there have been proposals put forth recently to detect network communities by using dynamical clustering. In this paper, we study how the feedback from dynamical clusters can shape the network connection weights with a weight-adaptation scheme motivated from Hebbian learning in neural systems. We show that such a scheme generically leads to the formation of community structure in globally coupled chaotic oscillators. The number of communities in the adaptive network depends on coupling strength c and adaptation strength r. In a modular network, the adaptation scheme will enhance the intramodule connection weights and weaken the intermodule connection strengths, generating effectively separated dynamical clusters that coincide with the communities of the network. In this sense, the modularity of the network is amplified by the adaptation. Thus, for a network with a strong community structure, the adaptation scheme can evidently reflect its community structure by the resulting amplified weighted network. For a network with a weak community structure, the statistical properties of synchronization clusters from different realizations can be used to amplify the modularity of the communities so that they can be detected reliably by the other traditional algorithms.

Uncovering Intrinsic Connectional Architecture of Functional Networks in Awake Rat Brain

Intrinsic connectional architecture of the brain is a crucial element in understanding the governing principle of brain organization. To date, enormous effort has been focused on addressing this issue in humans by combining resting-state functional magnetic resonance imaging (rsfMRI) with other techniques. However, this research area is significantly underexplored in animals, perhaps because of confounding effects of anesthetic agents used in most animal experiments on functional connectivity. To bridge this gap, we have systematically investigated the intrinsic connectional architecture in the rodent brain by using a previously established awake-animal imaging model. First, group independent component analysis was applied to the rsfMRI data to extract elementary functional clusters of the brain. The connectional relationships between these clusters, as evaluated by partial correlation analysis, were then used to construct a graph of whole-brain neural network. This network exhibited the typical features of small-worldness and strong community structures seen in the human brain. Finally, the whole-brain network was segregated into community structures using a graph-based analysis. The results of this work provided a functional atlas of intrinsic connectional architecture of the rat brain at both intraregion and interregion levels. More importantly, the current work revealed that functional networks in rats are organized in a nontrivial manner and conserve fundamental topological properties that are also seen in the human brain. Given the high psychopathological relevance of network organization of the brain, this study demonstrated the feasibility of studying mechanisms and therapies of multiple neurological and psychiatric diseases through translational research.

Seasonal Dynamics of Anammox Bacteria in Estuarial Sediment of the Mai Po Nature Reserve Revealed by Analyzing the 16S rRNA and Hydrazine Oxidoreductase (hzo) Genes

The community and population dynamics of anammox bacteria in summer (wet) and winter (dry) seasons in estuarial mudflat sediment of the Mai Po Nature Reserve were investigated by 16S rRNA and hydrazine oxidoreductase (hzo) genes. 16S rRNA phylogenetic diversity showed that sequences related to 'Kuenenia' anammox bacteria were presented in summer but not winter while 'Scalindua' anammox bacteria occurred in both seasons and could be divided into six different clusters. Compared to the 16S rRNA genes, the hzo genes revealed a relatively uniform seasonal diversity, with sequences relating to 'Scalindua', 'Anammoxoglobus', and planctomycete KSU-1 found in both seasons. The seasonal specific bacterial groups and diversity based on the 16S rRNA and hzo genes indicated strong seasonal community structures in estuary sediment of this site. Furthermore, the higher abundance of hzo genes in summer than winter indicates clear seasonal population dynamics. Combining the physicochemical characteristics of estuary sediment in the two seasons and their correlations with anammox bacteria community structure, we proposed the strong seasonal dynamics in estuary sediment of Mai Po to be due to the anthropogenic and terrestrial inputs, especially in summer, which brings in freshwater anammox bacteria, such as 'Kuenenia', interacting with the coastal marine anammox bacteria 'Scalindua'.

Uncovering intrinsic connectional architecture of functional networks in awake rat brain

Uncovering intrinsic connectional architecture of functional networks in awake rat brain

Communities in Italian corporate networks

The community structure of two real-world financial networks, namely the board network and the ownership network of the firms of the Italian Stock Exchange, is analyzed by means of the maximum modularity approach. The main result is that both networks exhibit a strong community structure and, moreover, that the two structures overlap significantly. This is due to a number of reasons, including the existence of pyramidal groups and directors serving in several boards. Overall, this means that the “small world” of listed companies is actually split into well identifiable “continents” (i.e., the communities).

Dynamics and Control of Diseases in Networks with Community Structure

The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc.) depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

The Naming Game in social networks: community formation and consensus engineering

We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat. Mech.: Theory Exp. P06014] in empirical social networks. This stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.

Epidemic spreading in weighted scale-free networks with community structure

Many empirical studies reveal that the weights and community structure are ubiquitous in variousnatural and artificial networks. In this paper, based on the SI disease model, we investigate theepidemic spreading in weighted scale-free networks with community structure. Two exponents,α andβ, areintroduced to weight the internal edges and external edges, respectively; and a tunable probabilityparameter q is also introduced to adjust the strength of community structure. We find the external weighting exponentβ plays a much more important role in slackening the epidemic spreading and reducingthe danger brought by the epidemic than the internal weighting exponentα. Moreover, a novel result we find is that the strong community structure is no longerhelpful for slackening the danger brought by the epidemic in the weighted cases. Inaddition, we show the hierarchical dynamics of the epidemic spreading in the weightedscale-free networks with communities which is also displayed in the famous BA scale-freenetworks.

Spatial and temporal variability in a stratified hypersaline microbial mat community

Hypersaline microbial mat communities have recently been shown to be more diverse than once thought. The variability in community composition of hypersaline mats, both in terms of spatial and temporal dimensions, is still poorly understood. Because this information is essential to understanding the complex biotic and abiotic interactions within these communities, terminal restriction fragment analysis and 16S rRNA gene sequencing were used to characterize the near-surface community of a hypersaline microbial mat in Guerrero Negro, Mexico. Core samples were analyzed to assay community variability over large regional scales (centimeter to kilometer) and to track depth-related changes in population distribution at 250-microm intervals over a diel period. Significant changes in total species diversity were observed at increasing distances across the mat surface; however, key species (e.g. Microcoleus sp.) were identified throughout the mat. The vertical position and abundance of >50% of the 60 peaks detected varied dramatically over a diel cycle, including Beggiatoa sp., cyanobacteria, Chloroflexus sp., Halochromatium sp., Bacteroidetes sp. and several as-yet-identified bacteria. Many of these migrations correlated strongly with diel changes in redox conditions within the mat, contributing to strong day-night community structure differences.

Epidemic spreading in scale-free networks with community structure

Many real networks consist of communities: groups of nodes within which connections aredense, but among which connections are sparser. In this paper, based on thesusceptible–infected (SI) model, epidemic prevalence is compared between two kinds ofscale-free network, with and without community structure, which have the same degreedistribution. It is found that networks with strong community structure are helpful forreducing the danger brought by epidemic prevalence in the network, which isshown by the comparison results of the prevalence scale, the variability of theprevalence scale and the average degree of new infected nodes at each time step. Wealso note that when facing networks with strong community structure, specialattention should be paid to nodes that are close to the initial infected seeds.

Optimal ranking in networks with community structure

The World-Wide Web (WWW) is characterized by a strong community structure in which groups of webpages (e.g. those devoted to a common topic or belonging to the same organization) are densely interconnected by hyperlinks. We study how such network architecture affects the average Google rank of individual communities. Using a mean-field approximation, we quantify how the average Google rank of community webpages depend on the degree to which it is isolated from the rest of the world in both incoming and outgoing directions, and α the only intrinsic parameter of Google's PageRank algorithm. Based on this expression we introduce a concept of a web-community being decoupled or conversely coupled to the rest of the network. We proceed with empirical study of several internal web-communities within two US universities. The predictions of our mean-field treatment were qualitatively verified in those real-life networks. Furthermore, the value α = 0.15 used by Google seems to be optimized for the degree of isolation of communities as they exist in the actual WWW.

Creating demand for sanitation and hygiene through Community Health Clubs: A cost-effective intervention in two districts in Zimbabwe

Unless strategies are found to galvanise rural communities and create a demand for sanitation, we cannot achieve the United Nations Millennium Development Goal of halving the 2.4 billion people without sanitation by the year 2015. This study describes an innovative methodology used in Zimbabwe—Community Health Clubs—which significantly changed hygiene behaviour and built rural demand for sanitation. In 1 year in Makoni District, 1244 health promotion sessions were held by 14 trainers, costing an average of US$0.21 per beneficiary and involving 11,450 club members (68,700 beneficiaries). In Tsholotsho District, 2105 members participated in 182 sessions held by three trainers which cost US$ 0.55 for each of the 12,630 beneficiaries. Within 2 years, 2400 latrines had been built in Makoni, and in Tsholotsho latrine coverage rose to 43% contrasted to 2% in the control area, with 1200 latrines being built in 18 months. Although Zimbabwe has historically relied on subsidies to stimulate sanitation, this intervention shows how total sanitation could be achievable. The remaining 57% of club members without latrines in Tsholotsho all practised faecal burial, a method previously unknown to them. Club members’ hygiene was significantly different ( p < 0.0001 ) from a control group across 17 key hygiene practices including hand washing, showing that if a strong community structure is developed and the norms of a community are altered, sanitation and hygiene behaviour are likely to improve. This methodology could be scaled up to contribute to ambitious global targets.

Structure of growing social networks.

We propose some simple models of the growth of social networks, based on three general principles: (1). meetings take place between pairs of individuals at a rate that is high if a pair has one or more mutual friends and low otherwise; (2). acquaintances between pairs of individuals who rarely meet decay over time; (3). there is an upper limit on the number of friendships an individual can maintain. Using computer simulations, we find that models that incorporate all of these features reproduce many of the features of real social networks, including high levels of clustering or network transitivity and strong community structure in which individuals have more links to others within their community than to individuals from other communities.

Community, Ethnicity and Neighbourhood

The empirical focus is two small adjacent estates in West Newcastle; one with a mostly Bangladeshi population, the other white. The contrasts between them form the basis for a discussion of low housing demand and high turnover; forms of social exclusion; community and ethnicity. The Bangladeshi population experience exclusion from housing. Their estate has high demand and low turnover. Their housing options are highly constrained by a combination of low income and fear of crime and harassment. On the other hand they are comparatively well integrated into the local economy and a strong community structure. In contrast, the white population has more housing choice as a result of low demand, but exhibit greater exclusion from the labour market and from 'civil society'. Recent proposals to restructure the housing of the West End could increase housing opportunities for the Bangladeshis, but their impact on the white estate is more uncertain.