Community Structure In Networks Research Articles

Characterizing the Bitcoin network topology with Node‐Probe

AbstractIn blockchain networks, topology discovery is a prerequisite when investigating the network characteristics (e.g., performance and robustness), which can provide a deeper comprehension of the behavior of the nodes and topology dynamicity. In this paper, we conduct a longitudinal study on the Bitcoin topology by collecting network snapshots from 2018 to 2022 with Node‐Probe, our topology discovery technique that uses recursive scanning to find all reachable nodes in the Bitcoin network. Using Node‐Probe, we have collected 5‐week‐long snapshots (36‐day‐long snapshots) of the Bitcoin main network and analyzed the network properties, community structure, and topology dynamicity. We confirm that our approach achieves a precision of 99% with a recall of 98% in inferring the topology. Analytical results on community structure show that the Bitcoin network has more communities than what should be expected from a random network. Meanwhile, analytical results on dynamicity indicate that the topology stands firmly on heavy and long‐running nodes. Improving the propagation mechanism using master nodes could improve the propagation delay by proximity compared with the Bitcoin default protocol. Considering a K‐anonymity attack, any transaction from one of the autonomous systems containing only a single Bitcoin node can easily be linked to real users' IP information.

Molecular Ecological Network Structure and Potential Function of the Bacterial Community in the Soil Profile under Indigenous Tree Plantations in Subtropical China

The soil profile is a strong and complex physicochemical gradient that greatly affects bacterial community structure and function between soil layers. However, little is known about molecular ecological network structure and bacterial community function under differing soil profiles in planted forests. Four typical native tree species (Pinus massoniana Lamb., Castanopsis hystrix Miq., Mytilaria laosensis Lec., and Michelia macclurei Dandy) plantations were selected from subtropical China as the research object. We evaluated molecular ecological network structure as well as potential function of the soil bacterial community at different soil depths (0–20, 20–40, and 40–60 cm) within native tree plantations. Our results showed that (1) compared to the topsoil (0–20 cm), the bacterial molecular ecological network scale increased within the middle layer (20–40 cm) and the subsoil (40–60 cm), and the interaction between species was stronger; (2) module hubs and connectors were the key bacterial groups in each soil layer and increased with increasing soil depth; (3) the dominant functional groups of the bacterial communities in each soil layer were chemoheterotrophy, aerobic chemoheterotrophy, cellulolysis, ureolysis, nitrogen fixation, and nitrate reduction, and they were related to soil carbon and nitrogen cycling; and (4) the different molecular ecological network structures along with relative bacterial functional group abundances among diverse soil layers were mainly affected by soil organic carbon (SOC), NO3−-N, NH4+-N, available phosphorus (AP), and total phosphorus (TP). Our study provides a theoretical foundation for bacterial community structure together with function within soil profiles of native tree plantations in subtropical regions.

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

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

Random search immune algorithm for community detection

Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in turn is combined with purely stochastic operators. According to the obtained outcomes, opt-IA shows strictly better performances than almost all heuristics and metaheuristics to which it was compared; whilst it turns out to be comparable with the Hyper-Heuristic method. Overall, it can be claimed that opt-IA, even if driven by a purely random process, proves to be reliable and with efficient performance. Furthermore, to prove the latter claim, a sensitivity analysis of the functionality was conducted, using the classic metrics NMI, ARI and NVI.

Clustering of multi-layer networks with structural relations and conservation of features

Multi-layer networks are omnipresent in nature and society, as complex systems often involve multiple types of relationships among entities. Compared with single-layer graphs, community detection is a challenging task that requires simultaneous characterization of the structure and relations across various layers. Current algorithms have been criticized for their suboptimal performance in describing and quantifying the structure of communities in multi-layer networks. A novel algorithm, called McNMF, is proposed to detect the conserved communities in multi-layer networks, where the shared and layer-specific features of vertices are learned simultaneously by utilizing joint nonnegative matrix factorization. Specifically, McNMF joins the feature learning, feature decomposition, and relation of the shared and layer-specific features into an overall objective function, thereby enhancing the quality of features. To learn the features of vertices, McNMF jointly factorizes matrices associated with multi-layer networks by projecting the topology into different basis spaces. It then decomposes the learned features into shared and layer-specific parts, enabling quantification of multi-layer network specificity at the feature level. Moreover, the relation between the shared and layer-specific features of vertices is measured using trace optimization, providing a comprehensive strategy to characterize the structure of conserved communities in multi-layer networks. Extensive experiments on artificial and real-world multi-layer networks demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods in terms of accuracy.

Abnormal brain network community structure related to psychological stress in schizophrenia.

Recent functional imaging studies in schizophrenia consistently report a disruption of brain connectivity. However, most of these studies analyze the brain connectivity during resting state. Since psychological stress is a major factor for the emergence of psychotic symptoms, we sought to characterize the brain connectivity reconfiguration induced by stress in schizophrenia. We tested the hypothesis that an alteration of the brain's integration-segregation dynamic could be the result of patients with schizophrenia facing psychological stress. To this end, we studied the modular organization and the reconfiguration of networks induced by a stress paradigm in forty subjects (twenty patients and twenty controls), thus analyzing the dynamics of the brain in terms of integration and segregation processes by using 3T-fMRI. Patients with schizophrenia did not show statistically significant differences during the control task compared with controls, but they showed an abnormal community structure during stress condition and an under-connected reconfiguration network with a reduction of hub nodes, suggesting a deficit of integration dynamic with a greater compromise of the right hemisphere. These results provide evidence that schizophrenia has a normal response to undemanding stimuli but shows a disruption of brain functional connectivity between key regions involved in stress response, potentially leading to altered functional brain dynamics by reducing integration capacity and showing deficits recruiting right hemisphere regions. This could in turn underlie the hyper-sensitivity to stress characteristic of schizophrenia.

Outlier detection in social networks leveraging community structure

Social networks have become an important aspect of our modern times and are gradually becoming an integral means of communication worldwide. Overwhelming amounts of data are being transferred over social networks every day. Hence ensuring security becomes a necessity. Suspicious users or spammers may pose a threat to the information and data shared by users over the network. With this in mind, outliers detection is a crucial aspect of network communication. In our paper, a new technique is proposed to identify the anomalies in a network from a global perspective by using the network community structure. In general, state-of-the-art outliers detection algorithms mainly focus on the individual nodes and their direct neighborhood. But our technique considers only those nodes which tend to belong to multiple communities or whose neighbors belong to the same community or do not belong to any community. Results after experiments on synthetic and real-world networks show an improvement of 7-10% and 29% in F-Score and Jaccard similarity, respectively, compared to the state-of-the-art algorithms. Furthermore, we achieve almost 1.83 times speedup compared to the state-of-the-art algorithms.

Community assembly and network structure of epiphytic and endophytic phyllosphere fungi in a subtropical mangrove ecosystem.

Microorganisms can influence plant growth and health, ecosystem functioning, and stability. Community and network structures of mangrove phyllosphere fungi have rarely been studied although mangroves have very important ecological and economical values. Here, we used high throughput sequencing of the internal transcribed spacer 2 (ITS2) to assess epiphytic and endophytic phyllosphere fungal communities of six true mangrove species and five mangrove associates. Totally, we obtained 1,391 fungal operational taxonomic units (OTUs), including 596 specific epiphytic fungi, 600 specific endophytic fungi, and 195 shared fungi. The richness and community composition differed significantly for epiphytes and endophytes. Phylogeny of the host plant had a significant constraint on epiphytes but not endophytes. Network analyses showed that plant-epiphyte and plant-endophyte networks exhibited strong specialization and modularity but low connectance and anti-nestedness. Compared to plant-endophyte network, plant-epiphyte network showed stronger specialization, modularity, and robustness but lower connectance and anti-nestedness. These differences in community and network structures of epiphytes and endophytes may be caused by spatial niche partitioning, indicating their underlying ecological and environmental drivers are inconsistent. We highlight the important role of plant phylogeny in the assembly of epiphytic but not endophytic fungal communities in mangrove ecosystems.

Uncovering the Local Hidden Community Structure in Social Networks

Hidden community is a useful concept proposed recently for social network analysis. Hidden communities indicate some weak communities whose most members also belong to other stronger dominant communities. Dominant communities could form a layer that partitions all the individuals of a network, and hidden communities could form other layer(s) underneath. These layers could be natural structures in the real-world networks like students grouped by major, minor, hometown, and so on. To handle the rapid growth of network scale, in this work, we explore the detection of hidden communities from the local perspective, and propose a new method that detects and boosts each layer iteratively on a subgraph sampled from the original network. We first expand the seed set from a single seed node based on our modified local spectral method and detect an initial dominant local community. Then we temporarily remove the members of this community as well as their connections to other nodes, and detect all the neighborhood communities in the remaining subgraph, including some “broken communities” that only contain a fraction of members in the original network. The local community and neighborhood communities form a dominant layer, and by reducing the edge weights inside these communities, we weaken this layer’s structure to reveal the hidden layers. Eventually, we repeat the whole process, and all communities containing the seed node can be detected and boosted iteratively. We theoretically show that our method can avoid some situations that a broken community and the local community are regarded as one community in the subgraph, leading to the inaccuracy of detection which can be caused by global hidden community detection methods. Extensive experiments show that our method could significantly outperform the state-of-the-art baselines designed for either global hidden community detection or multiple local community detection.

Emergence of allostery through reorganization of protein residue network architecture.

Despite more than a century of study, consensus on the molecular basis of allostery remains elusive. A comparison of allosteric and non-allosteric members of a protein family can shed light on this important regulatory mechanism, and the bacterial biotin protein ligases, which catalyze post-translational biotin addition, provide an ideal system for such comparison. While the Class I bacterial ligases only function as enzymes, the bifunctional Class II ligases use the same structural architecture for an additional transcription repression function. This additional function depends on allosterically activated homodimerization followed by DNA binding. In this work, we used experimental, computational network, and bioinformatics analyses to uncover distinguishing features that enable allostery in the Class II biotin protein ligases. Experimental studies of the Class II Escherichia coli protein indicate that catalytic site residues are critical for both catalysis and allostery. However, allostery also depends on amino acids that are more broadly distributed throughout the protein structure. Energy-based community network analysis of representative Class I and Class II proteins reveals distinct residue community architectures, interactions among the communities, and responses of the network to allosteric effector binding. Bioinformatics mutual information analyses of multiple sequence alignments indicate distinct networks of coevolving residues in the two protein families. The results support the role of divergent local residue community network structures both inside and outside of the conserved enzyme active site combined with distinct inter-community interactions as keys to the emergence of allostery in the Class II biotin protein ligases.

Targeted Community Merging provides an efficient comparison between collaboration clusters and departmental partitions

Abstract Community detection theory is vital for the structural analysis of many types of complex networks, especially for human-like collaboration networks. In this work, we present a new community detection algorithm, the Targeted Community Merging algorithm, based on the well-known Girvan–Newman algorithm, which allows obtaining community partitions with high values of modularity and a small number of communities. We then perform an analysis and comparison between the departmental and community structure of scientific collaboration networks within the University of Zaragoza. Thus, we draw valuable conclusions from the inter- and intra-departmental collaboration structure that could be useful to take decisions on an eventual departmental restructuring.

Successional and phenological effects on plant‐floral visitor interaction networks of a tropical dry forest

Abstract Plant‐pollinator interactions are fundamental to ecosystem functioning; however, the role that succession and phenology have on these interactions is poorly understood, particularly in endangered tropical ecosystems. In highly diverse ecosystems such as tropical dry forests (TDF), variation in water and food availability determines the life cycles of animal pollinators. Therefore, understanding patterns of flowering phenology and plant‐pollinator interactions across seasons in successional environments is key to maintaining and restoring TDF. We analysed the functional dynamics of plant‐floral visitor interactions at the community level across a successional gradient in a Mexican TDF. We evaluated changes in the diversity of blooming plant species and floral visitors, phenological patterns, interaction network metrics and beta diversity among early, intermediate and late successional stages, between dry and rainy seasons. We found a higher diversity of blooming plant species and a higher richness of animal species in the intermediate and late successional stages. Peak abundance of floral visitors overlapped with flowering peaks in the late successional stages, but this was not consistently the case in the early and intermediate stages. Plant‐floral visitors networks differed in structure according to successional stage and season, but specialisation metrics were higher in late successional stages. Interaction networks were more dissimilar between dry and rainy seasons within successional stages than within seasons between successional stages, suggesting connectivity across successional sites during each season. In addition, closely related plant species do not share the same pollination systems in any successional stage. Synthesis. Our results showed that plant‐floral visitor interactions are dynamic and vary with flowering phenology and with successional changes in plant and animal diversity. Plant‐floral visitor interactions were more diverse and specialised in the late successional stages. In the rainy season, differences in network structure among successional stages are due to interaction rewiring, while in the dry season, it is caused by species turnover. Our results demonstrate that seasonality plays a key role in community diversity and network structure and highlight the importance of conserving mature forests to ensure the maintenance of critical pollination interactions across all successional stages.

Sobriety, social capital, and village network structures

External shocks or stimuli can have significant effects on community network structures, which in turn can affect relationship ties and social capital. To this point, however, few studies analyze impacts of self-help programs, such as those targeting substance abuse, on network structures or measures of social capital. This study analyzes the effects of a randomized control trial (RCT) in western Kenya that included an intervention aimed at decreasing alcohol abuse using group therapies and spousal level counseling. Using dyadic network data collected from alcohol-using households, the analysis finds that two nearby same-gender peers randomly assigned to the treatment group are 9 percentage points [pp] more likely to have an agricultural information link with their peer and 8 pp more likely to work with their peer compared to two individuals in the control group. Additional individual-level results find that assignment to the treatment significantly increases an individual’s network centrality as measured by degree, closeness, and betweenness. I explore potential mechanisms and find evidence suggesting network effects at the intensive and extensive margin and the development of support linkages all likely contributed to these results. This study shows that programs utilizing peer effects can not only have positive impacts on alcohol consumption, but also positive spillover effects on relationship ties and on a treated individual’s network centrality. The results thus suggest significant positive impacts of the program on social capital, and points to the indirect benefits of substance abuse programs (and self-help programs in general) on communities.

A time evolving online social network generation algorithm

The rapid growth of online social media usage in our daily lives has increased the importance of analyzing the dynamics of online social networks. However, the dynamic data of existing online social media platforms are not readily accessible. Hence, there is a necessity to synthesize networks emulating those of online social media for further study. In this work, we propose an epidemiology-inspired and community-based, time-evolving online social network generation algorithm (EpiCNet), to generate a time-evolving sequence of random networks that closely mirror the characteristics of real-world online social networks. Variants of the algorithm can produce both undirected and directed networks to accommodate different user interaction paradigms. EpiCNet utilizes compartmental models inspired by mathematical epidemiology to simulate the flow of individuals into and out of the online social network. It also employs an overlapping community structure to enable more realistic connections between individuals in the network. Furthermore, EpiCNet evolves the community structure and connections in the simulated online social network as a function of time and with an emphasis on the behavior of individuals. EpiCNet is capable of simulating a variety of online social networks by adjusting a set of tunable parameters that specify the individual behavior and the evolution of communities over time. The experimental results show that the network properties of the synthetic time-evolving online social network generated by EpiCNet, such as clustering coefficient, node degree, and diameter, match those of typical real-world online social networks such as Facebook and Twitter.

Mixed membership estimation for social networks

In economics and social science, network data are regularly observed, and a thorough understanding of the network community structure facilitates the comprehension of economic patterns and activities. Consider an undirected network with n nodes and K communities. We model the network using the Degree-Corrected Mixed-Membership (DCMM) model, where for each node i=1,2,…,n, there exists a membership vector πi=(πi(1),πi(2),…,πi(K))′, where πi(k) is the weight that node i puts on community k, 1≤k≤K. In comparison to the well-known stochastic block model (SBM), the DCMM permits both severe degree heterogeneity and mixed memberships, making it more realistic and general. We present an efficient approach, Mixed-SCORE, for estimating the mixed membership vectors of all nodes and the other DCMM parameters. This approach is inspired by the discovery of a delicate simplex structure in the spectral domain. We derive explicit error rates for the Mixed-SCORE algorithm and demonstrate that it is rate-optimal over a broad parameter space. Our findings provide a novel statistical tool for network community analysis, which can be used to understand network formations, extract nodal features, identify unobserved covariates in dyadic regressions, and estimate peer effects. We applied Mixed-SCORE to a political blog network, two trade networks, a co-authorship network, and a citee network, and obtained interpretable results.

A local community detection algorithm based on potential community exploration

Local community detection aims to detect local communities that have expanded from the given node. Because of the convenience of obtaining the local information of the network and nearly linear time complexity, researchers have proposed many local community detection algorithms to discover the community structure of real-world networks and have obtained excellent results. Most existing local community detection algorithms expand from the given node to a community based on an expansion mechanism that can determine the membership of nodes. However, when determining the membership of neighboring nodes of a community, previous algorithms only considered the impact from the current community, but the impact from the potential communities around the node was neglected. As the name implies, a potential community is a community structure hidden in an unexplored network around a node. This paper gives the definition of potential communities of a node for the first time, that is, a series of connected components consisting of the node’s neighbors that are in the unexplored network. We propose a three-stage local expansion algorithm, named LCDPC, that performs Local Community Detection based on Potential Community exploration. First, we search for a suitable node to replace the given node as the seed by calculating the node importance and the node similarity. Second, we form the initial community by combining the seed and its suitable potential community. Finally, the eligible nodes are selected by comparing the similarities between potential communities and the expanding community and nodes and adding them to the initial community for community expansion. The proposed algorithm is compared with eight state-of-the-art algorithms on both real-world networks and artificial networks, and the experimental results show that the performance of the proposed algorithm is better than that of the comparison algorithms and that the application of potential community exploration can help identify the community structure of networks.

Detecting communities in complex networks using triangles and modularity density

The community structure is an important property of complex networks. Community detection of complex networks can be used to help reveal the relationship between topology and functionality. Modularity density QD, as a community quality function, has been widely used for detection of community structure in networks. Here, we develop a novel modularity density based on triangular motifs, and also prove its equivalence with the spectral clustering and non-negative matrix factorization. In addition, we develop a community detection method that incorporates the edge and triangular motif information, and apply the proposed method to several classical complex networks. Experiments show that the proposed method outperforms the standard modularity density QD.

Overlapping community‐based particle swarm optimization algorithm for influence maximization in social networks

AbstractInfluence maximization, whose aim is to maximise the expected number of influenced nodes by selecting a seed set of k influential nodes from a social network, has many applications such as goods advertising and rumour suppression. Among the existing influence maximization methods, the community‐based ones can achieve a good balance between effectiveness and efficiency. However, this kind of algorithm usually utilise the network community structures by viewing each node as a non‐overlapping node. In fact, many nodes in social networks are overlapping ones, which play more important role in influence spreading. To this end, an overlapping community‐based particle swarm optimization algorithm named OCPSO for influence maximization in social networks, which can make full use of overlapping nodes, non‐overlapping nodes, and their interactive information is proposed. Specifically, an overlapping community detection algorithm is used to obtain the information of overlapping community structures, based on which three novel evolutionary strategies, such as initialisation, mutation, and local search are designed in OCPSO for better finding influential nodes. Experimental results in terms of influence spread and running time on nine real‐world social networks demonstrate that the proposed OCPSO is competitive and promising comparing to several state‐of‐the‐arts (e.g. CGA, CMA‐IM, CIM, CDH‐SHRINK, CNCG, and CFIN).

Different patterns and drivers of fungal communities between phyllosphere and rhizosphere in alpine grasslands

Abstract Epiphytic fungi are vital in enhancing their host plant performance and can also cause plant diseases. Generally, phyllosphere fungal community are majorly driven by climate, while rhizosphere fungal community are determined by soil properties and spatial distance. However, the differences in the relative effects of environmental factors on fungal community compositions and network structures remain far from clear between phyllosphere and rhizosphere. In this study, we conducted a large‐scale field survey along a 1400 km transect in the Tibetan Plateau and explored the composition and structure of phyllosphere and rhizosphere fungal communities from two dominant grass species (Leontopodium nanum and Stipa purpurea). L. nanum is widely distributed in relatively wet areas of alpine grasslands but S. purpurea prefers relatively dry areas. The geographical distributions of these two species overlap in the middle of the transect First, we found that precipitation was more important than temperature to affect fungal alpha diversity. High precipitation significantly promoted fungal alpha diversity in both phyllosphere and rhizosphere. Second, climate and spatial variables explained more variations in fungal community in the phyllosphere than rhizosphere. Specifically, greater precipitation promoted the relative abundances of pathotrophic fungi in the phyllosphere and rhizosphere, whereas lower precipitation only stimulated the relative abundances of symbiotrophic fungi in the rhizosphere. Third, precipitation had different impacts on phyllosphere and rhizosphere fungal networks between host species. Drought caused lower node number of fungal networks in the phyllosphere and rhizosphere of L. nanum. However, for S. purpurea, drought led to more complex and positive fungal networks in the phyllosphere and rhizosphere. Overall, these results indicated that precipitation caused different fungal community compositions along the transect between phyllosphere and rhizosphere, but consistently shaped their fungal networks. This study is among the first to provide compelling evidence on the large‐scale spatial variations and controlling factors for epiphytic fungal community in alpine grasslands. These new findings help to understand the role of epiphytic fungal community in affecting the functions of alpine grasses to cope with extreme environments. Read the free Plain Language Summary for this article on the Journal blog.

Identification of disease modules using higher-order network structure.

Higher-order interaction patterns among proteins have the potential to reveal mechanisms behind molecular processes and diseases. While clustering methods are used to identify functional groups within molecular interaction networks, these methods largely focus on edge density and do not explicitly take into consideration higher-order interactions. Disease genes in these networks have been shown to exhibit rich higher-order structure in their vicinity, and considering these higher-order interaction patterns in network clustering have the potential to reveal new disease-associated modules. We propose a higher-order community detection method which identifies community structure in networks with respect to specific higher-order connectivity patterns beyond edges. Higher-order community detection on four different protein-protein interaction networks identifies biologically significant modules and disease modules that conventional edge-based clustering methods fail to discover. Higher-order clusters also identify disease modules from genome-wide association study data, including new modules that were not discovered by top-performing approaches in a Disease Module DREAM Challenge. Our approach provides a more comprehensive view of community structure that enables us to predict new disease-gene associations. https://github.com/Reed-CompBio/graphlet-clustering.