Community Structure In Networks Research Articles

Plant-pollinator network architecture does not impact intraspecific microbiome variability.

Variation in how individuals interact with food resources can directly impact, and be affected by, their microbial interactions due to the potential for transmission. The degree to which this transmission occurs, however, may depend on the structure of forager networks, which determine the community-scale transmission opportunities. In particular, how the community-scale opportunity for transfer balances individual-scale barriers to transmission is unclear. Examining the bee-flower and bee-microbial interactions of over 1000 individual bees, we tested (1) the degree to which individual floral visits predicted microbiome composition and (2) whether plant-bee networks with increased opportunity for microbial transmission homogenized the microbiomes of bees within that network. The pollen community composition carried by bees was associated with microbiome composition at some sites, suggesting that microbial transmission at flowers occurred. Contrary to our predictions, however, microbiome variability did not differ based on transfer opportunity: bee microbiomes in asymmetric networks with high opportunity for microbial transfer were similarly variable compared to microbiomes in networks with more evenly distributed links. These findings suggest that microbial transmission at flowers is frequent enough to be observed at the community level, but that community network structure did not substantially change the dynamics of this transmission, perhaps due to filtering processes in host guts.

Long-Horizon Associative Learning Explains Human Sensitivity to Statistical and Network Structures in Auditory Sequences.

Networks are a useful mathematical tool for capturing the complexity of the world. In a previous behavioral study, we showed that human adults were sensitive to the high-level network structure underlying auditory sequences, even when presented with incomplete information. Their performance was best explained by a mathematical model compatible with associative learning principles, based on the integration of the transition probabilities between adjacent and nonadjacent elements with a memory decay. In the present study, we explored the neural correlates of this hypothesis via magnetoencephalography (MEG). Participants (N = 23, 16 females) passively listened to sequences of tones organized in a sparse community network structure comprising two communities. An early difference (∼150 ms) was observed in the brain responses to tone transitions with similar transition probability but occurring either within or between communities. This result implies a rapid and automatic encoding of the sequence structure. Using time-resolved decoding, we estimated the duration and overlap of the representation of each tone. The decoding performance exhibited exponential decay, resulting in a significant overlap between the representations of successive tones. Based on this extended decay profile, we estimated a long-horizon associative learning novelty index for each transition and found a correlation of this measure with the MEG signal. Overall, our study sheds light on the neural mechanisms underlying human sensitivity to network structures and highlights the potential role of Hebbian-like mechanisms in supporting learning at various temporal scales.

Effects of protection and temperature variation on temporal stability in a marine reserve network.

Understanding the drivers of ecosystem stability has been a key focus of modern ecology as the impacts of the Anthropocene become more prevalent and extreme. Marine protected areas (MPAs) are tools used globally to promote biodiversity and mediate anthropogenic impacts. However, assessing the stability of natural ecosystems and responses to management actions is inherently challenging due to the complex dynamics of communities with many interdependent taxa. Using a 12-year time series of subtidal community structure in an MPA network in the Channel Islands (United States), we estimated species interaction strength (competition and predation), prey species synchrony, and temporal stability in trophic networks, as well as temporal variation in sea surface temperature to explore the causal drivers of temporal stability at community and metacommunity scales. At the community scale, only trophic networks in MPAs at Santa Rosa Island showed greater temporal stability than reference sites, likely driven by reduced prey synchrony. Across islands, competition was sometimes greater and predation always greater in MPAs compared with reference sites. Increases in interaction strength resulted in lower temporal stability of trophic networks. Although MPAs reduced prey synchrony at the metacommunity scale, reductions were insufficient to stabilize trophic networks. In contrast, temporal variation in sea surface temperature had strong positive direct effects on stability at the regional scale and indirect effects at the local scale through reductions in species interaction strength. Although MPAs can be effective management strategies for protecting certain species or locations, our findings for this MPA network suggest that temperature variation has a stronger influence on metacommunity temporal stability by mediating species interactions and promoting a mosaic of spatiotemporal variation in community structure of trophic networks. By capturing the full spectrum of environmental variation in network planning, MPAs will have the greatest capacity to promote ecosystem stability in response to climate change.

Effects of Combined Stress of High Density Polyethylene Microplastics and Chlorimuron-ethyl on Soybean Growth and Rhizosphere Bacterial Community

With the vigorous development of agriculture in China， plastic mulch film and pesticides are widely used in agricultural production. However， the accumulation of microplastics （formed by the degradation of plastic mulch film） and pesticides in soil has also caused many environmental problems. At present， the environmental biological effects of microplastics or pesticides have been reported， but there are few studies on the combined effects on crop growth and the rhizosphere soil bacterial community. Therefore， in this study， the high density polyethylene microplastics （HDPE， 500 mesh） were designed to be co-treated with sulfonylurea herbicide chlorimuron-ethyl to study their effects on soybean growth. In addition， the effects of the combined stress of HDPE and chlorimuron-ethyl on soybean rhizosphere soil bacterial community diversity， structure composition， microbial community network， and soil function were investigated using high-throughput sequencing technology， interaction network， and PICRUSt2 function analysis to clarify the combined toxicity of HDPE and chlorimuron-ethyl to soybean. The results showed that the half-life of chlorimuron-ethyl in soil was prolonged by the 1% HDPE treatment （from 11.5 d to 14.3 d）， and the combined stress of HDPE and chlorimuron-ethyl had more obvious inhibition effects on soybean growth than that of the single pollutant or control. The HiSeq 2 500 sequencing showed that the rhizosphere bacterial community of soybean was composed of 20 phyla and 312 genera under combined stress， the number of phyla and genera was significantly less than that of the control and single pollutant treatment， and the relative abundances of bacteria with potential biological control and plant growth-promoting characteristics （such as Nocardioides and Sphingomonas） were reduced. Alpha diversity analysis showed that the combined stress significantly reduced the richness and diversity of the soybean rhizosphere bacterial community， and Beta diversity analysis showed that the combined stress significantly changed the structure of the bacterial community. The dominant flora of the rhizosphere bacterial community were regulated， and the abundances of secondary functional layers such as amino acid metabolism， energy metabolism， and lipid metabolism were reduced under combined stress by the analysis of LEfSe and PICRUSt2. It was inferred from the network analysis that the combined stress of HDPE and chlorimuron-ethyl reduced the total number of connections and network density of soil bacteria， simplified the network structure， and changed the important flora species to maintain the stability of the network. The results above indicated that the combined stress of HDPE and chlorimuron-ethyl significantly affected the growth of soybean and changed the rhizosphere bacterial community structure， soil function， and network structure. Compared with that of the single pollutant treatment， the potential risk of combined stress was greater. The results of this study can provide guidance for evaluating the ecological risks of polyethylene microplastics and chlorimuron-ethyl and for the remediation of contaminated soil.

Parallel Particle Swarm Optimization Algorithm for Identifying Complex Communities in Biological Networks

Identification of complex communities in biological networks is a critical and ongoing challenge since lots of network-related problems correspond to the subgraph isomorphism problem known in the literature as NP-hard. Several optimization algorithms have been dedicated and applied to solve this problem. The main challenge regarding the application of optimization algorithms, specifically to handle large-scale complex networks, is their relatively long execution time. Thus, this paper proposes a parallel extension of the PSO algorithm to detect communities in complex biological networks. The main contribution of this study is summarized in three- fold; Firstly, a modified PSO algorithm with a local search operator is proposed to detect complex biological communities with high quality. Secondly, the variability in the capability of PSO to extract community structure in biological networks is studied when different types of crossover operators are used. Finally, to reduce the computational time needed to solve this problem, especially when detecting complex communities in large-scale biological networks, we have implemented parallel computing to execute the algorithm. The performance of the proposed algorithm was tested and evaluated on two real biological networks. The experimental results showed the effective performance of the proposed algorithm when using single-point crossover operator, and its superiority over other counterpart algorithms. Moreover, the use of parallel computing in the proposed algorithm representation has greatly reduced the computational time required for its execution.

Unveiling community structures in static networks through graph variational Bayes with evolution information

With the rapid development of graph variational Bayes theory, some representative community detection methods have been proposed. Although these methods are well designed, they are limited by the inherent constraints of static networks. Therefore, how to re-examine the information in static networks from a new perspective becomes a challenge. To this end, in this paper, we propose EG-VGAE and its variant EGC-VGAE, going beyond the constraints of static networks from a new perspective. Specifically, we first demonstrate that the evolution relations on static networks can be simulated reasonably. And then, our EG-VGAE method combines evolution information with static network information to realize a fine-grained propagation of local low-frequency signals to global low-frequency signals, thereby improving the accuracy of community assignment for each node. Building on this progress, our method EGC-VGAE imposes the smoothness constraint on adjacent slices, significantly enhancing the sensitivity of the method to evolution information and mitigating the impact of network noise. The comprehensive experimental results on real static networks well validate that our methods outperform state-of-the-art methods in most cases. The code is available at https://github.com/GDM-SCNU/EG-VGAE.

Deciphering Differences in Microbial Community Diversity between Clubroot-Diseased and Healthy Soils.

Clubroot (Plasmodiophora brassicae) is an important soilborne disease that causes severe damage to cruciferous crops in China. This study aims to compare the differences in chemical properties and microbiomes between healthy and clubroot-diseased soils. To reveal the difference, we measured soil chemical properties and microbial communities by sequencing 18S and 16S rRNA amplicons. The available potassium in the diseased soils was higher than in the healthy soils. The fungal diversity in the healthy soils was significantly higher than in the diseased soils. Ascomycota and Proteobacteria were the most dominant fungal phylum and bacteria phylum in all soil samples, respectively. Plant-beneficial microorganisms, such as Chaetomium and Sphingomonas, were more abundant in the healthy soils than in the diseased soils. Co-occurrence network analysis found that the healthy soil networks were more complex and stable than the diseased soils. The link number, network density, and clustering coefficient of the healthy soil networks were higher than those of the diseased soil networks. Our results indicate that the microbial community diversity and network structure of the clubroot-diseased soils were different from those of the healthy soils. This study is of great significance in exploring the biological control strategies of clubroot disease.

A cascading failure model of cyber-coupled power system considering virus propagation

Cyber attacks from the information layer can have a serious impact on the operation of the cyber-coupled power system. In this paper, a practical cascading failure model used for investigating the impact of virus propagation on the cascading failure of cyber-coupled power system is proposed. The model takes into account the actual functionality of cyber-coupled system and the dynamic virus propagation in cyber network, and describes the effects of cyber node failure caused by virus infection and power flow overload on the cascading failure. Moreover, the impact of defense upgrade on the virus propagation is considered. Centralized control based on dispatching center and local regulation based on static power-frequency characteristic are introduced to describe the actual monitoring function of cyber system on the power grid. Then, the effects of defense upgrade strategy and modular community structure on the failure evolution are analyzed. The results indicate that the spread of virus destroys the monitoring function of cyber network on the power network and leads to more serious failure results. The effectiveness of defense upgrade strategies is related to the upgrade probability of information stations. In addition, the stronger community structure of cyber network can improve the robustness of cyber-coupled power system.

Identifying influential spreaders in complex networks based on density entropy and community structure

In recent years, exploring the relationship between community structure and node centrality in complex networks has gained significant attention from researchers, given its fundamental theoretical significance and practical implications. To address the impact of network communities on target nodes and effectively identify highly influential nodes with strong propagation capabilities, this paper proposes a novel influential spreaders identification algorithm based on density entropy and community structure (DECS). The proposed method initially integrates a community detection algorithm to obtain the community partition results of the networks. It then comprehensively considers the internal and external density entropies and degree centrality of the target node to evaluate its influence. Experimental validation is conducted on eight networks of varying sizes through susceptible–infected–recovered (SIR) propagation experiments and network static attack experiments. The experimental results demonstrate that the proposed method outperforms five other node centrality methods under the same comparative conditions, particularly in terms of information spreading capability, thereby enhancing the accurate identification of critical nodes in networks.

Analysis of the spatial correlation pattern of logistics carbon emission efficiency and its influencing factors: the case of China.

As logistics carbon emission efficiency is an essential industry linking regions, investigating this issue from a spatial correlation perspective is practically significant. Utilizing data from 282 prefecture-level cities spanning 2006 to 2019, we used a super slacks-based measure model, a modified gravity model, motif analysis, the Infomap algorithm, and an exponential random graph model to analyze the spatial correlation patterns and influencing factors of logistics carbon emission efficiency. The following conclusions were drawn. (1) The spatial correlation of logistics carbon emission efficiency during the study period exhibited a core-edge pattern, with the central region emerging as a high-correlation hub. (2) The scale of the spatial association network community of carbon emission efficiency in the logistics industry changed constantly, and the stability of the network community structure gradually increased. From a microstructural perspective, the dispersed-mode structure was a pivotal element in the formation of the spatial correlation network of logistics carbon emission efficiency. (3) Node interaction tendencies were a critical force driving network formation. Financial investment, government concern, international openness, population density, and innovation ability were conducive to the formation of spatial correlations of logistics carbon emission efficiency.

Analyzing user migration in blockchain online social networks through network structure and discussion topics of communities on multilayer networks

User migration, i.e. the movement of large sets of users from one online social platform to another one, is one of the main phenomena occurring in modern online social networks and even involves the most recent alternative paradigms of online social networks, such as blockchain online social networks (BOSNs). In these platforms, user migration mainly occurs through hard forks of the supporting blockchain, i.e. a split of the original blockchain and the creation of an alternative blockchain, to which users may decide to migrate. However, our understanding of user migration and its mechanisms is still limited, particularly regarding the role of densely connected user groups (communities) during migration and fork events. Are there differences between users who stay and those who decide to leave, in terms of network structure and discussion topics? In this work, we show, through network-based analysis centered on the identification of communities on multilayer networks and text mining that a) the “position” of a group within the network of social and economic interactions is connected to the likelihood of a group to migrate, i.e. marginal groups are more likely to leave; b) group network structure is also important, as users in densely connected groups interacting through monetary transactions are more likely to stay; c) users who leave are characterized by different discussion topics; and d) user groups interacting through monetary transactions show interest in migration-related content if they are going to leave. These findings highlight the importance of social and economic relationships between users during a user migration caused by fork events In general, in the larger context of online social media, it motivates the need to investigate user migration through a network-inspired approach based on groups and specific subgraphs while leveraging user-generated content, at the same time.

A new community detection method for simplified networks by combining structure and attribute information

Complex networks have a large number of nodes and edges, which prevents the understanding of network structure and the discovery of valid information. This paper proposes a new community detection method for simplified networks. First, a similarity measure is defined, the path and attribute information can reflect the potential relationship between nodes that are not directly connected. Based on the defined similarity, an Importance Score(IS) is constructed to show the importance of each node, it reflects the density around each node. Then, the simplification processes can be realized on complex networks. On the simplified network, this paper proposes a novel community detection method, in which the community structure of the simplified network is detected. The experiments were conducted on real networks and compared with several widely used methods. The experimental results illustrate that the proposed method is more advantageous and can visually and effectively uncover the community structure.

On the role of diffusion dynamics on community-aware centrality measures.

Theoretical and empirical studies on diffusion models have revealed their versatile applicability across different fields, spanning from sociology and finance to biology and ecology. The presence of a community structure within real-world networks has a substantial impact on how diffusion processes unfold. Key nodes located both within and between these communities play a crucial role in initiating diffusion, and community-aware centrality measures effectively identify these nodes. While numerous diffusion models have been proposed in literature, very few studies investigate the relationship between the diffusive ability of key nodes selected by community-aware centrality measures, the distinct dynamical conditions of various models, and the diverse network topologies. By conducting a comparative evaluation across four diffusion models, utilizing both synthetic and real-world networks, along with employing two different community detection techniques, our study aims to gain deeper insights into the effectiveness and applicability of the community-aware centrality measures. Results suggest that the diffusive power of the selected nodes is affected by three main factors: the strength of the network's community structure, the internal dynamics of each diffusion model, and the budget availability. Specifically, within the category of simple contagion models, such as SI, SIR, and IC, we observe similar diffusion patterns when the network's community structure strength and budget remain constant. In contrast, the LT model, which falls under the category of complex contagion dynamics, exhibits divergent behavior under the same conditions.

Visual contrast sensitivity is associated with community structure integrity in cognitively unimpaired older adults: the Brain Networks and Mobility (B-NET) Study

Older adults with impairment in contrast sensitivity (CS), the ability to visually perceive differences in light and dark, are more likely to demonstrate limitations in mobility function, but the mechanisms underlying this relationship are poorly understood. We sought to determine if functional brain networks important to visual processing and mobility may help elucidate possible neural correlates of this relationship. This cross-sectional analysis utilized functional MRI both at rest and during a motor imagery (MI) task in 192 community-dwelling, cognitively-unimpaired older adults ≥ 70 years of age from the Brain Networks and Mobility study (B-NET). Brain networks were partitioned into network communities, groups of regions that are more interconnected with each other than the rest of the brain, the spatial consistency of the communities for multiple brain subnetworks was assessed. Lower baseline binocular CS was significantly associated with degraded sensorimotor network (SMN) community structure at rest. During the MI task, lower binocular CS was significantly associated with degraded community structure in both the visual (VN) and default mode network (DMN). These findings may suggest shared neural pathways for visual and mobility dysfunction that could be targeted in future studies.

The Contribution of Social Networks to Marketing Strategies

It has existed since ancient times, and sociology has conducted in-depth research on it. Sociology believes that society is not composed of individuals but of networks, which contain nodes and their relationships. It can also be understood that our social relationships are intertwined, forming a complex social network. In recent decades, social network analysis has been widely concerned and applied in academic research and practical applications. By analyzing and measuring network graph, it reveals key nodes in social networks, information transmission channels and community structures in networks. Since social networks have a significant impact on the operation and efficiency of the market, this tool is gradually being applied to market research. This paper will focus on three types of contributions of the network to marketing from the perspective of sellers. This paper will show different network diagrams, and describe and analyze these diagrams to reach the corresponding conclusion, that is, the contribution of the network to marketing.

Towards improving community detection in complex networks using influential nodes

Abstract Complex network analysis is inspired by empirical studies of real-world networks such as computer networks, technology networks and social networks. The analysis of community structure in complex networks is understood as an important issue by the research society. A community is a set of nodes in a network where the density of connections is high. The insight in the literature shows many approaches to identify influential nodes, but these approaches only lead to finding community centres. Meanwhile, clustering techniques are effectively used for community detection, where they can reveal group structure and hidden connections by considering topological and demographic information. This article presents an ensemble clustering algorithm based on influential nodes to improve community detection in complex networks. Considering different characteristics of the network, the proposed method seeks to discover common interests between users and their behaviours to identify the most suitable communities. First, a set of influential nodes are identified as community centres. Then, these centres are considered as cluster centres. After that, primary clusters are created based on the determined centres. Finally, the primary clusters are reclustered to form the final clusters. Here, the final clusters are considered as communities of users in the network. The simulation has been performed on real-world networks and the results confirm the effectiveness of the proposed method. Specifically, the communities identified by the proposed method are 2.1% better than the best existing state-of-the-art method in terms of modularity. Keywords: complex network; community detection; influential nodes; ensemble clustering.

Short-term continuous monocropping reduces peanut yield mainly via altering soil enzyme activity and fungal community

Continuous monocropping can lead to soil sickness and increase of soil-borne disease, which finally reduces crop yield. Microorganisms benefit plants by increasing nutrient availability, participating in auxin synthesis, and defending against pathogens. However, little is known about the influence of short-term successive peanuts cropping on soil properties, enzyme activities, its yield, plant-associated microbes, and their potential correlations in peanut production. Here, we examined the community structure, composition, network structure and function of microbes in the rhizosphere and bulk soils under different monocropping years. Moreover, we assessed the impact of changes in the soil micro-environment and associated soil microbes on peanut yield. Our results showed that increase of monocropping year significantly decreased most soil properties, enzyme activities and peanut yield (p < 0.05). Principal co-ordinates analysis (PCoA) and analysis of similarities (ANOSIM) indicated that monocropping year significantly influenced the fungal community structure in the rhizosphere and bulk soils (p < 0.01), while had no effect on the bacterial community. With the increase of continuous monocropping year, peanut selectively decreased (e.g., Candidatus_Entotheonella, Bacillus and Bryobacter) or increased (e.g., Nitrospira, Nocardioides, Ensifer, Gaiella, and Novosphingobium) the abundance of some beneficial bacterial genera in the rhizosphere. Continuous monocropping significantly increased the abundance of plant pathogens (e.g., Plectosphaerella, Colletotrichum, Lectera, Gibberella, Metarhizium, and Microdochium) in the rhizosphere and negatively affected the balance of fungal community. Besides, these species were correlated negatively with L-leucine aminopeptidase (LAP) activity. Network co-occurrence analysis showed that continuous monocropping simplified the interaction network of bacteria and fungi. Random forest and partial least squares path modeling (PLS-PM) analysis further showed that fungal community, pathogen abundance, soil pH, and LAP activity negatively affected peanut yield. In conclusion, short-term continuous monocropping decreased LAP activity and increased potential fungal pathogens abundance, leading to reduction of peanut yield.

Improved brain community structure detection by two-step weighted modularity maximization

The human brain can be regarded as a complex network with interacting connections between brain regions. Complex brain network analyses have been widely applied to functional magnetic resonance imaging (fMRI) data and have revealed the existence of community structures in brain networks. The identification of communities may provide insight into understanding the topological functions of brain networks. Among various community detection methods, the modularity maximization (MM) method has the advantages of model conciseness, fast convergence and strong adaptability to large-scale networks and has been extended from single-layer networks to multilayer networks to investigate the community structure changes of brain networks. However, the problems of MM, suffering from instability and failing to detect hierarchical community structure in networks, largely limit the application of MM in the community detection of brain networks. In this study, we proposed the weighted modularity maximization (WMM) method by using the weight matrix to weight the adjacency matrix and improve the performance of MM. Moreover, we further proposed the two-step WMM method to detect the hierarchical community structures of networks by utilizing node attributes. The results of the synthetic networks without node attributes demonstrated that WMM showed better partition accuracy than both MM and robust MM and better stability than MM. The two-step WMM method showed better accuracy of community partitioning than WMM for synthetic networks with node attributes. Moreover, the results of resting state fMRI (rs-fMRI) data showed that two-step WMM had the advantage of detecting the hierarchical communities over WMM and was more insensitive to the density of the rs-fMRI networks than WMM.

Obfuscating Community Structure in Complex Network With Evolutionary Divide-and-Conquer Strategy

As the number of social network users grows exponentially with increasingly complex profiles, community detection algorithms play a critical role in user portrait analysis. The associated privacy concerns, however, have not sufficiently received the attention that it deserves. In this work, we investigate methods for obfuscating the original community structure by modifying a small number of connections imperceptibly so as to protect the privacy of users. The existing evolutionary models have some successes in this type of NP-hard problem but can only be applied to small-scale datasets, rendering them inadequate for real-world applications. To alleviate this problem, we propose an original and novel CoeCo, a cooperative evolutionary community obfuscation model. In CoeCo, we leverage the divide-and-conquer strategy and put forward a co-evolutionary optimization algorithm suitable for community structure, in which two different fitness functions promote each other to find the optimal edge set. In addition, the motif hypergraph and permanence are used to improve population initialization. The experimental results indicate that our proposed method can achieve excellent efficacy in obfuscating community structure and also greatly reduces running time.

Identifying Community Structures in Social Networks using Discrete Harmony Search Algorithm

Identifying Community Structures in Social Networks using Discrete Harmony Search Algorithm