Community Structure In Networks Research Articles

Comparative analysis of community composition and network structure between phyllosphere endophytic and epiphytic fungal communities of Mussaenda pubescens.

The phyllosphere constitutes a critical habitat for microorganisms, exerting profound influences on host vitality, developmental dynamics, reproductive functions, and stress resilience. However, the diversity and network structure of endophytic and epiphytic fungal communities within this microecosystem have not been thoroughly explored. In this investigation, high-throughput sequencing technologies were employed to assess the diversity, community composition, and network structure of endophytic and epiphytic fungal communities associated with Mussaenda pubescens across six geographically distinct locations in Southeast China. The results revealed significant differences in community composition and diversity between endophytic and epiphytic fungi, with pronounced geographical variation observed within these phyllosphere fungal communities. Network analysis indicated that epiphytic fungal networks possess enhanced complexity compared with their endophytic counterparts, although the latter exhibit greater network stability. Moreover, stochastic processes were identified as pivotal in shaping the composition of these fungal communities. This research substantially enriches our comprehension of the diversity and organizational mechanisms of phyllosphere fungal communities, providing novel insights into the modalities of species coexistence and the stability of community equilibrium within ecosystems.IMPORTANCEThis study employs high-throughput sequencing technologies to explore the fungal communities within the phyllosphere of Mussaenda pubescens across Southeast China, offering significant insights into plant mycobiome. It demonstrates geographical variations in these fungal communities, with epiphytic fungi exhibiting more complex interaction networks compared with the endophytic fungi. Crucially, the research indicates that stochastic processes play a substantial role in the composition of fungal communities. These findings enhance our comprehension of plant-associated microecosystems and underscore the intricate interplay of randomness in maintaining ecosystem stability and diversity.

Collective effect of self-learning and social learning on language dynamics: a naming game approach in social networks.

Linguistic rules form the cornerstone of human communication, enabling people to understand and interact with one another effectively. However, there are always irregular exceptions to regular rules, with one of the most notable being the past tense of verbs in English. In this work, a naming game approach is developed to investigate the collective effect of social behaviours on language dynamics, which encompasses social learning, self-learning with preference and forgetting due to memory constraints. Two features that pertain to individuals' influential ability and affinity are introduced to assess an individual's role of social influence and discount the information they communicate in the Bayesian inference-based social learning model. Our findings suggest that network heterogeneity and community structure significantly impact language dynamics, as evidenced in synthetic and real-world networks. Furthermore, self-learning significantly enhances the process of language regularization, while forgetting has a relatively minor impact. The results highlight the substantial influence of network structure and social behaviours on the transition of opinions, from consensus to polarization, demonstrating its importance in language dynamics. This work sheds new light on how individual learners adopt language rules through the lenses of complexity science and decision science, advancing our understanding of language dynamics.

Kernel machine tests of association using extrinsic and intrinsic cluster evaluation metrics.

Modeling the network topology of the human brain within the mesoscale has become an increasing focus within the neuroscientific community due to its variation across diverse cognitive processes, in the presence of neuropsychiatric disease or injury, and over the lifespan. Much research has been done on the creation of algorithms to detect these mesoscopic structures, called communities or modules, but less has been done to conduct inference on these structures. The literature on analysis of these community detection algorithms has focused on comparing them within the same subject. These approaches, however, either do not accomodate a more general association between community structure and an outcome or cannot accommodate additional covariates that may confound the association of interest. We propose a semiparametric kernel machine regression model for either a continuous or binary outcome, where covariate effects are modeled parametrically and brain connectivity measures are measured nonparametrically. By incorporating notions of similarity between network community structures into a kernel distance function, the high-dimensional feature space of brain networks, defined on input pairs, can be generalized to non-linear spaces, allowing for a wider class of distance-based algorithms. We evaluate our proposed methodology on both simulated and real datasets.

Observing the dynamic community structure of urban travel networks based on navigation data

Dynamic community division is crucial for comprehending the interactive structure and mechanism between residents and space. Currently, there is insufficient research on dynamic community changes in travel networks regarding travel distance. This study utilizes residents’ travel trajectories to construct a multi-layer network based on travel purposes. The study establishes methods for identifying travel network communities and analyzing community evolution influence. Dynamic characteristics of travel network communities at different distances are extracted. Results show that within 3–7 km travel distance, different travel network communities exhibit continuous spatial aggregation, whereas beyond 11 km, heterogeneity is observed. Moreover, communities within 3-7 km experience drastic dynamic evolution, whereas beyond 7 km, they tend to stabilize, forming a large-scale and fixed travel mode. This study enriches the dynamic interaction research of functional space from the perspective of distance, and the experimental results provide an effective reference for urban planning and management.

Bird flock effect-based dynamic community detection: Unravelling network patterns over time

Community structure is essential for topological analysis, function study, and pattern detection in complex networks. As establishing community structure in a dynamic network is difficult, it gives a unique perspective in many interdisciplinary fields. Many researchers have explored the challenging technique that requires parameter specification and optimization for quality result. This study proposed an eco-system conceptual framework based on bird flock effect. Relying on the natural law of rule, we designed a dynamic community detection named DCDBFE. The design of algorithm was based on the three basic rules of bird flock: separation, alignment, and cohesion phase. Then, we provide an explanation of similarity measure used between vertices to identify the modules attraction. DCDBFE employs an incremental community detection approach to repeatedly detect communities in each network snapshot or time step. The contributions are obtained for high quality community detected, free-parameter and well stability. To test its performance, extensive experiments were conducted on both synthetic and real-world networks. The outcomes demonstrate that our approach can effectively find satisfaction from each time step by comparison with the other well-known algorithms.

Intra-striatal dopaminergic inter-subject covariance in social drinkers and non-treatment-seeking alcohol use disorder participants.

One of the neurobiological correlates of alcohol use disorder (AUD) is the disruption of striatal dopaminergic function. Although regional differences in dopamine (DA) tone/function have been well studied, interregional relationships (represented as inter-subject covariance) have not been investigated and may offer a novel avenue for understanding DA tone. Positron emission tomography (PET) data with [11C]raclopride in 22 social drinking controls and 17 AUD participants were used to generate group-level striatal covariance (partial Pearson correlation) networks, which were compared edgewise as well as on global network metrics and community structure. An exploratory analysis examined the impact of tobacco cigarette use status. Striatal covariance was validated in an independent publicly available [18F]fallypride PET sample of healthy volunteers. Striatal covariance of control participants from both data sets showed a clear bipartition of the network into two distinct communities, one in the anterior and another in the posterior striatum. This organization was disrupted in the AUD participants' network, which showed significantly lower network metrics compared with the control participants' network. Stratification by cigarette use suggests differential consequences on group covariance networks. This work demonstrates that network neuroscience can quantify group differences in striatal DA and that its interregional interactions offer new insight into the consequences of AUD.

Effects of water tables and nitrogen application on soil bacterial community diversity, network structure, and function in an alpine wetland, China

Effects of water tables and nitrogen application on soil bacterial community diversity, network structure, and function in an alpine wetland, China

Evidence of community structure in phonological networks of multiple languages.

Thousands of phonological word forms known to a speaker can be organised as a lexical network using the tools of network science. In these networks, nodes represent words and edges are placed between phonological neighbours. Previous work has shown that phonological networks of various languages have similar macrolevel network properties. The present study aimed to investigate if phonological networks of different languages also have similar mesolevel properties, specifically, the presence of robust community structure. Prior community detection analyses revealed robust community structure for English. Community detection analyses conducted on French, German, Dutch, and Spanish networks indicate that all networks showed strong evidence of community structure-mesolevel clustering of word forms whereby larger communities tended to contain shorter, frequent words with many phonological neighbours. Words of the same community tended to share similar phonotactic structures. Results suggest that the organisation of phonological word forms in language are governed by similar principles that could have important implications for lexical processing. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Supplemental Material for Evidence of Community Structure in Phonological Networks of Multiple Languages

Supplemental Material for Evidence of Community Structure in Phonological Networks of Multiple Languages

Com_Tracker: a two-phases framework for detecting and tracking community evolution in dynamic social networks

PurposeThis paper aims to introduce a novel modularity-based framework, Com_Tracker, designed to detect and track community structures in dynamic social networks without recomputing them from scratch at each snapshot. Despite extensive research in this area, existing approaches either require repetitive computations or fail to capture key community behavioral events, both of which limit the ability to generate timely and actionable insights. Efficiently tracking community structures is crucial for real-time decision-making in rapidly evolving networks, while capturing behavioral events is necessary for understanding deeper community dynamics. This study addresses these limitations by proposing a more efficient and adaptive solution. It aims to answer the following questions: How can we efficiently track community structures without recomputation? How can we detect significant community events over time?Design/methodology/approachCom_Tracker models dynamic social networks as a sequence of snapshots. First, it detects the community structure of the initial snapshot using a static community detection algorithm. Then, for each subsequent time step, Com_Tracker updates the community structure based on the previous snapshot, allowing it to track communities and detect their changes over time. The locus-based adjacency encoding scheme is adopted, and Pearson’s correlation guides the construction of neighboring solutions.FindingsExperiments conducted on various networks demonstrate that Com_Tracker effectively detects community structures and tracks their evolution in dynamic social networks. The results highlight its potential for real-time tracking and provide promising performance outcomes.Practical implicationsCom_Tracker offers valuable insights into community evolution, helping practitioners across fields such as resource management, public security, marketing and public health. By understanding how communities evolve, decision-makers can better allocate resources, enhance targeted strategies and predict future community behaviors, improving overall responsiveness to changes in network dynamics.Originality/valueCom_Tracker addresses critical gaps in existing research by combining the strengths of modularity maximization with efficient tracking of community changes. Unlike previous methods that either recompute structures or fail to capture behavioral events, Com_Tracker provides an incremental, adaptive framework capable of detecting both community evolution and behavioral changes, enhancing real-world applicability in dynamic environments.

Identification of dynamic networks community by fusing deep learning and evolutionary clustering

Community detection is a critical component of network analysis and a hot topic in social computing. Detecting community structure in dynamic networks has important theoretical and practical implications for understanding the intrinsic function of networks and predicting network behavior. However, the majority of existing dynamic community detection methods adopt shallow models, which have limited ability to excavate complex non-linear structures and tend to generate undesirable community structures. In order to obtain an accurate and robust community structure in dynamic networks, we are inspired by network representation learning and utilize the deep learning to detect evolving communities in dynamic networks. In this paper, we propose a novel dynamic community detection method by fusing Deep Learning and Evolutionary Clustering (DLEC). This work attempts to combine deep learning and evolutionary clustering into a unified framework. First, we propose a matrix construction strategy to fully reveal the inherent community structures via the underlying community memberships. Then, we develop a novel multi-layer deep autoencoder framework that consists of multiple non-linear functions to extract the latent deep representation of the dynamic network. Based on the evolutionary clustering framework, a graph regularization term is introduced to ensure the smoothness of the community evolution. Finally, we employ the K-means clustering algorithm on the low-dimensional network space to obtain the community structure. Extensive experimental results on synthetic and real-world networks show that the proposed DLEC algorithm can effectively detect high-quality communities in dynamic networks.

Rhizosphere microbial community assembly as influenced by reductive soil disinfestation to resist successive cropping obstacle.

Reductive soil disinfestation (RSD), which involves creating anaerobic conditions and incorporating large amounts of organic materials into the soil, has been identified as a reliable strategy for reducing soilborne diseases in successive cropping systems. However, limited research exists on the connections between soil microorganism composition and plant diseases under various types of organic material applications. This study aimed to evaluate the effects of distinct RSD strategies (control without soil amendment; RSD with 1500 kg ha-1 molasses powder; RSD with 3000 kg ha-1 molasses powder; RSD with 3000 kg ha-1 molasses powder and 37.5-41.3 kg ha-1 microbial agent) on the plant disease index, bacterial community composition and network structure in rhizosphere soil. RSD treatments significantly reduced the occurrence of black shank disease in tobacco and increased soil bacterial diversity. High amounts of molasses powder in RSD treatments further enhanced disease inhibition and reduced fungal abundance and Shannon index. RSD also increased the relative abundance of bacterial phylum Firmicutes and fungal phylum Ascomycota, while decreasing the relative abundance of bacterial phyla Chloroflexi and Acidobacteriota and fungal phylum Basidiomycota in rhizosphere soil. A multiple regression model identified bacterial positive cohesion as the primary factor influencing the plant disease index, with a greater impact than bacterial negative cohesion and community stability. The competition among beneficial bacteria for creating a healthy rhizosphere environment is likely a key factor in the success of RSD in reducing plant disease risk. RSD, especially with higher rates of molasses powder, is a viable strategy for controlling black shank disease in tobacco and promoting soil health by fostering beneficial microbial communities. This study provides guidelines for soil management and plant disease prevention. © 2024 Society of Chemical Industry.

Characterization of Industrial Carbon Emission Dynamics and Network Structure Evolution in China

Given that industry is the major source of carbon emissions, clarifying the carbon transfer regularity triggered by industry production linkage is of great importance in realizing the synergistic development of industry to reduce pollution and carbon emissions. Based on the perspective of responsibility allocation, the study accounted for the industry carbon emissions and constructed a carbon transfer weighted directed network model and applied the social network analysis method to explore the structure of the industry carbon transfer network and its evolution characteristics from 2005 to 2020. The results showed that： ① The overall carbon emissions of industries from 2005 to 2020 presented a rapid growth trend and large differences occurred in emissions between industries. ② The scale of the carbon transfer network was expanding rapidly and the characteristics of the small-world network were significant. ③ The community structure of the carbon transfer network was obvious, including four optimal associations： high carbon emission, carbon transmission, carbon endogenous, and high spillover. ④ The construction industry and the metal smelting and rolling processing industry were the core of the carbon transfer network structure, with strong network control. In view of the above characteristics, we proposed countermeasures and suggestions in terms of implementing the allocation of carbon emission reduction responsibilities and differentiated emission reduction measures for associations as well as focusing on the management of core nodes of the network, with the intent of providing cross-sectoral synergistic emission reduction ideas for the development of sustainable low-carbon transformation in China's industry sector.

Enhancing robustness of community structure in networks against attacks with gray information

In recent years, network science has received significant attention and application. As an important mesoscale structure of networks, community structure reveals the inherent modular structure and potential functionality of networks. Therefore, maintaining the community structure during attacks is crucial. Existing studies on community robustness primarily focus on two attack behaviors: malicious attacks and random failures. However, little is known on the community robustness in the more practical situations where the attackers have limited ability to access precise network information, leading to gray information attack behaviors. Hence, in this paper, we investigate the robustness of network communities under gray information attacks and the enhancing algorithms. Following the community robustness measure for the sequential attacks, we establish a unified evaluation framework for attacks with gray information by introducing a gray attack coefficient, which treats the usual random failures and malicious attacks as the two special situations in the proposed framework. Several enhancing algorithms with local search strategy are exquisitely devised. The efficacy of our framework and algorithms which significantly improve the community robustness against gray attacks is demonstrated by the experimental results on a variety of real-world networks. Our findings have important implications not only in enhancing the community robustness of existing networks but also in designing robust ones from the scratch, which paves the way to further understand and seek strategies and solutions for network community robustness with gray information.

Urbanization reduces the stability of soil microbial community by reshaping the diversity and network complexity

Rapid urbanization considerably alters soil environment, biodiversity, and stability of terrestrial ecosystems. Soil microbial community, a key component of global biodiversity, plays a pivotal role in ecosystem stability and is highly vulnerable to urbanization. However, effects of urbanization on the diversity, stability, and network structure of soil microbial community remain poorly understood. Herein, we investigated the diversity and stability of soil microbial communities, including bacteria, fungi, and protists, across three regions with different levels of urbanization—urban, suburb, and ecoregion—using high-throughput sequencing techniques. Our results revealed that urbanization led to a notable decrease in the alpha diversity of soil microbial community, causing a significant reduction in soil stability, as assessed by the average variation degree (AVD). The loss of stability was linked to the diminished alpha diversity of the soil fungal and protistan communities, along with weakened interactions among bacteria, fungi, and protists. Notably, the majority of keystone species identified through network analysis were classified as bacteria (Proteobacteria) and displayed a strong positive correlation with the environmental factors influencing AVD. This highlights that the variability of bacteria and the immutability of fungi and protists are important to sustain soil microbial stability. Furthermore, structural equation models indicated that protistan diversity primarily drove soil microbial stability across all regions studied. In the suburban and ecoregion areas, soil microbial stability was directly influenced by the soil properties, bacterial diversity, and keystone species, as well as indirectly affected by heavy metals. These results underscore how urbanization can reduce the stability of soil microbial community via declined diversity and network complexity, whereas the establishment of ecoregions maybe contribute to preserve the diversity and stability of soil microbial community.

Dynamic Silos: Increased Modularity and Decreased Stability in Intraorganizational Communication Networks During the COVID-19 Pandemic

Workplace communications around the world were drastically altered by the COVID-19 pandemic, related work-from-home orders, and the rise of remote work. To understand these shifts, we analyzed aggregated, anonymized metadata from over 360 billion emails within 4,361 organizations worldwide. By comparing month-to-month and year-over-year metrics, we examined changes in network community structures over the 24 months before and after COVID-19. We also examined shifts across multiple communication media (email, instant message, video call, and calendaring software) within a single global organization and compared them with communications shifts that were driven by changes in formal organizational structure. We found that in 2020, organizations around the world became more siloed than in 2019, evidenced by increased modularity. This shift was concurrent with decreased stability within silos. Collectively, our analyses indicate that following the onset of COVID-19, employees’ communications began to shift more dynamically between subcommunities (teams, workgroups, or functional areas). At the same time, once in a subcommunity, they limited their communication to other members of that community. We term these network changes dynamic silos. We provide initial insights into the meaning and implications of dynamic silos for the future of work. This paper was accepted by Lamar Pierce, organizations. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.02797 .

Local core expanding-based label diffusion and local deep embedding for fast community detection algorithm in social networks

Community detection is a key task in social network analysis, as it reveals the underlying structure and function of the network. Various global and local techniques exist for uncovering community structures in social networks wherein diffusion-based algorithms are proposed as novel methods for local community detection, particularly suited for large-scale networks. The efficacy of diffusion processes and initial detection is paramount in the successful identification of community structures within social networks. This effectiveness hinges significantly on the meticulous selection of the label diffuser core, which serves as the foundation for propagating labels through the network, and the precise labeling of boundary nodes. Addressing the constraints of current community detection algorithms, notably their time complexity and efficiency, this paper proposes a novel local community detection algorithm that combines core expansion with label diffusion, and deep embedding techniques. In the proposed method, a new centrality measure is introduced for appropriate core selection to facilitate precise label diffusion in the initial phase. Subsequently, a deep embedding technique is employed for updating labels of boundary and core nodes using the GraphSage embedding method. Finally, a rapid merging step is executed to amalgamate initially proximate communities into finalized community structures in large-scale social networks. We evaluate our algorithm on 14 real-world and 4 synthetic networks and show that it outperforms existing methods in terms of NMI, F-measure, ARI, and modularity. According to numerical results, the proposed method shows approximately 1.04 %, 1.03 %, and 1.12 % improvement in F-measure, NMI, and ARI measures respectively, compared to the second-best method, LBLD, in the networks with ground-truth. In addition, our method is able to accurately identify communities in large-scale networks such as Orkut, YouTube, and LiveJournal, where it ranks among the top-performing methods. Our approach exhibits the best performance in terms of ARI compared to other algorithms under comparison.

Persistence, changes and robustness of nest webs along a latitudinal gradient in the Canadian boreal forest

IntroductionIn eastern Canada, the boreal forest is associated with an important latitudinal shift in forest composition and structure, which occurs in the transition between the mixed southern boreal forest and the coniferous northern boreal forest. Along this transition, upland mixedwood stands with large deciduous trees (important for cavity-dependent vertebrate species) are gradually replaced by forests with smaller conifer trees, primarily black spruce (Picea mariana). Concomitantly, the availability of lowland forests flooded by the American beaver (Castor canadensis), which can provide adequate conditions for tree-cavity users, is also decreasing.MethodsWe hypothesized that this latitudinal gradient would bring important changes in the functional diversity and network structure of vertebrate cavity-using communities. Along this latitudinal gradient we used a nest web approach to analyze the structure and robustness of networks of cavity users in upland forests and in lowland forests flooded by beavers.ResultsDespite their low availability in the northern forest region, we found that mixedwood stands persisted throughout the boreal forest in being the main drivers of nest webs network structure of upland forests whereas old black spruce stands contribution was low. In lowland forests, beaver ponds harbored nest webs with a rich and complex structure in both forest regions. Species removal simulations revealed that across our latitudinal gradient upland and lowland forest nest webs responded differently. In upland forests, the removal of trembling aspen and the Pileated Woodpecker (Dryocopus pileatus) caused the highest proportions of secondary extinctions, showing low robustness of nest webs given that these two species were highly connected to the other species. Contrastingly, nest webs in beaver ponds were more robust mainly because excavator species used a higher diversity of tree species despite the removal of the Northern Flicker (Colaptes auratus) which induced numerous secondary extinctions. The Pileated Woodpecker remained the pivotal species across the two forest regions in upland forests whereas the Northern Flicker became the main large cavity provider in beaver ponds across the latitudinal gradient.DiscussionWe discuss how mixedwood forests and beaver ponds, which are key habitat types for the cavity-using vertebrate community across our latitudinal gradient, should be maintained and protected in landscapes under industrial timber harvesting.

Endophytic and epiphytic metabarcoding reveals fungal communities on cashew phyllosphere in Kenya.

Plants intimately coexist with diverse taxonomically structured microbial communities that influence host health and productivity. The coexistence of plant microbes in the phyllosphere benefits biodiversity maintenance, ecosystem function, and community stability. However, differences in community composition and network structures of phyllosphere epiphytic and endophytic fungi are widely unknown. Using Illumina Miseq sequencing of internal transcribed spacer (ITS) and 28S rRNA gene amplicons, we characterised the epiphytic and endophytic fungal communities associated with cashew phyllosphere (leaf, flower and fruit) from Kwale, Kilifi and Lamu counties in Kenya. The ITS and 28S rRNA gene sequences were clustered into 267 and 108 operational taxonomic units (OTUs) at 97% sequence similarity for both the epiphytes and endophytes. Phylum Ascomycota was abundant followed by Basidiomycota, while class Saccharomycetes was most dominant followed by Dothideomycetes. The major non-ascomycete fungi were associated only with class Tremellales. The fungal communities detected had notable ecological functions as saprotrophs and pathotrophs in class Saccharomyectes and Dothideomycetes. The community composition of epiphytic and endophytic fungi significantly differed between the phyllosphere organs which was statistically confirmed by the Analysis of Similarity test (ANOSIM Statistic R: 0.3273, for 28S rRNA gene and ANOSIM Statistic R: 0.3034 for ITS). The network analysis revealed that epiphytic and endophytic structures were more specialized, modular and had less connectance. Our results comprehensively describe the phyllosphere cashew-associated fungal community and serve as a foundation for understanding the host-specific microbial community structures among cashew trees.

Network structure and taxonomic composition of tritrophic communities of Fagaceae, cynipid gallwasps and parasitoids in Sichuan, China

Abstract A key question in insect community ecology is whether parasitoid assemblages are structured by the food plants of their herbivore hosts. Tritrophic communities centred on oak‐feeding cynipid gallwasps are one of the best‐studied tritrophic insect communities. Previous work suggests that host plant identity is a much stronger predictor of oak–cynipid interactions than of cynipid–parasitoid interactions. However, these relationships have not been formally quantified. We reason that the potential for ‘bottom‐up’ effects should increase with host plant phylogenetic diversity. We, therefore, generated quantified interaction network data for previously unstudied tritrophic cynipid communities in Sichuan, China, where, in addition to Quercus, cynipid host plants include Castanea, Castanopsis and Lithocarpus. We characterise these communities taxonomically and compare the extent to which host plant taxonomy predicts plant–herbivore and plant–parasitoid associations. We sampled 42,620 cynipid galls of 176 morphotypes from 23 host plant species, yielding over 4500 specimens of 64 parasitoid morphospecies. Many parasitoids were identifiable to chalcidoid taxa present in other Holarctic oak cynipid communities, with the addition of Cynipencyrtus (Cynipencyrtidae). As elsewhere, Sichuan parasitoid assemblages were dominated by generalists. Gallwasp–plant interaction networks were significantly more modular than parasitoid–plant association networks. Gallwasps were significantly more specialised to host plants (i.e. had higher mean d' values) than parasitoids. Parasitoid assemblages nevertheless showed significant plant‐associated beta diversity, with a dominant turnover component. We summarise parallels between our study and other Fagaceae‐associated cynipid communities and discuss our findings in light of the processes thought to structure tritrophic interactions centred on endophytic insect herbivores.