Showing papers on "Modularity (networks) published in 2019"
TL;DR: It is found that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations.
Abstract: The roles of different nodes within a network are often understood through centrality analysis, which aims to quantify the capacity of a node to influence, or be influenced by, other nodes via its connection topology. Many different centrality measures have been proposed, but the degree to which they offer unique information, and whether it is advantageous to use multiple centrality measures to define node roles, is unclear. Here we calculate correlations between 17 different centrality measures across 212 diverse real-world networks, examine how these correlations relate to variations in network density and global topology, and investigate whether nodes can be clustered into distinct classes according to their centrality profiles. We find that centrality measures are generally positively correlated to each other, the strength of these correlations varies across networks, and network modularity plays a key role in driving these cross-network variations. Data-driven clustering of nodes based on centrality profiles can distinguish different roles, including topological cores of highly central nodes and peripheries of less central nodes. Our findings illustrate how network topology shapes the pattern of correlations between centrality measures and demonstrate how a comparative approach to network centrality can inform the interpretation of nodal roles in complex networks.
168 citations
TL;DR: It is proved that the trace optimization of multi-layer modularity density is equivalent to the objective functions of algorithms, such as kernel-means, nonnegative matrix factorization (NMF), spectral clustering and multi-view clustering, for multi- layer networks, which serves as the theoretical foundation for designing algorithms for community detection.
Abstract: Many complex systems are composed of coupled networks through different layers, where each layer represents one of many possible types of interactions. A fundamental question is how to extract communities in multi-layer networks. The current algorithms either collapses multi-layer networks into a single-layer network or extends the algorithms for single-layer networks by using consensus clustering. However, these approaches have been criticized for ignoring the connection among various layers, thereby resulting in low accuracy. To attack this problem, a quantitative function (multi-layer modularity density) is proposed for community detection in multi-layer networks. Afterward, we prove that the trace optimization of multi-layer modularity density is equivalent to the objective functions of algorithms, such as kernel $K$ -means, nonnegative matrix factorization (NMF), spectral clustering and multi-view clustering, for multi-layer networks, which serves as the theoretical foundation for designing algorithms for community detection. Furthermore, a S emi- S upervised j oint N onnegative M atrix F actorization algorithm ( S2-jNMF ) is developed by simultaneously factorizing matrices that are associated with multi-layer networks. Unlike the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the S2-jNMF algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method outperforms the state-of-the-art approaches for community detection in multi-layer networks.
119 citations
TL;DR: This work focuses on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms, and introduces deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network.
Abstract: Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of a large interdisciplinary community of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed in order to better understand the impact of community structure and its dynamics on networked systems. Here, we first focus on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms. We discuss modularity and the use of modularity maximization as the basis for community detection. Then, we follow with an overview of the Stochastic Block Model and its different variants as well as inference of community structures from such models. Next, we focus on time evolving networks, where existing nodes and links can disappear, and in parallel new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over the last decade. We briefly discuss considerable advances made in this field recently. Finally, we focus on immunization strategies essential for targeting the influential spreaders of epidemics in modular networks. Their main goal is to select and immunize a small proportion of individuals from the whole network to control the diffusion process. Various strategies have emerged over the years suggesting different ways to immunize nodes in networks with overlapping and non-overlapping community structure. We first discuss stochastic strategies that require little or no information about the network topology at the expense of their performance. Then, we introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network.
107 citations
TL;DR: It is proposed that brain network modularity, a measure of brain subnetwork segregation, is a unifying biomarker of intervention-related plasticity and provides a foundation for developing targeted, personalized interventions to improve cognition.
99 citations
TL;DR: The multi-layer, multi-subject framework proposed here represents an advancement over current approaches by straighforwardly mapping community assignments across subjects and holds promise for future investigations of inter-subject community variation in clinical populations or as a result of task constraints.
85 citations
TL;DR: This work introduces and formalizes the problem of community detection attack and develops efficient strategies to attack community detection algorithms by rewiring a small number of connections, leading to privacy protection.
Abstract: Community detection plays an important role in social networks, since it can help to naturally divide the network into smaller parts so as to simplify network analysis. However, on the other hand, it arises the concern that individual information may be overmined, and the concept community deception has been proposed to protect individual privacy on social networks. Here, we introduce and formalize the problem of community detection attack and develop efficient strategies to attack community detection algorithms by rewiring a small number of connections, leading to privacy protection. In particular, we first give two heuristic attack strategies, i.e., Community Detection Attack (CDA) and Degree Based Attack (DBA), as baselines, utilizing the information of detected community structure and node degree, respectively. Then, we propose an attack strategy called “genetic algorithm (GA)-based Q-Attack,” where the modularity $Q$ is used to design the fitness function. We launch community detection attack based on the above three strategies against six community detection algorithms on several social networks. By comparison, our Q-Attack method achieves much better attack effects than CDA and DBA, in terms of the larger reduction of both modularity $Q$ and normalized mutual information (NMI). In addition, we further take transferability tests and find that adversarial networks obtained by Q-Attack on a specific community detection algorithm also show considerable attack effects while generalized to other algorithms.
75 citations
TL;DR: This work partitions an experimentally tractable regulatory network—the gap gene system of dipteran insects—using an alternative approach and shows that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour.
Abstract: The existence of discrete phenotypic traits suggests that the complex regulatory processes which produce them are functionally modular. These processes are usually represented by networks. Only modular networks can be partitioned into intelligible subcircuits able to evolve relatively independently. Traditionally, functional modularity is approximated by detection of modularity in network structure. However, the correlation between structure and function is loose. Many regulatory networks exhibit modular behaviour without structural modularity. Here we partition an experimentally tractable regulatory network-the gap gene system of dipteran insects-using an alternative approach. We show that this system, although not structurally modular, is composed of dynamical modules driving different aspects of whole-network behaviour. All these subcircuits share the same regulatory structure, but differ in components and sensitivity to regulatory interactions. Some subcircuits are in a state of criticality, while others are not, which explains the observed differential evolvability of the various expression features in the system.
75 citations
01 Sep 2019
TL;DR: This paper looks into realization of private 5G networks in the vertical domains, with a focus on the smart factory, and provides a systematic classification of the potential deployment architecture and operation models.
Abstract: Fifth generation of mobile communication network (5G) technology enables, for the first time, realization of stand-alone, standard-based private wireless networks to support a broad range of demanding applications across a variety of vertical industries. This is achieved, among other features, due to the flexibility, modularity and programmability of the 5G system architecture. The flexibility enables a large number of deployment and operation models, which on the other hand could make it a challenge to find the model fitting best for a particular vertical industry. Therefore, this paper, looks into realization of private 5G networks in the vertical domains, with a focus on the smart factory, and provide a systematic classification of the potential deployment architecture and operation models. In addition to that, a comprehensive set of evaluation metrics are identified and a comparative analysis of the identified deployment and operation models are presented.
62 citations
04 Feb 2019
TL;DR: A systematic evaluation of 12 commonly used participant-level confound regression strategies designed to mitigate the effects of micromovements in a sample of 393 youths indicates variability in the effectiveness of the evaluated pipelines across benchmarks.
Abstract: Dynamic functional connectivity reflects the spatiotemporal organization of spontaneous brain activity in health and disease. Dynamic functional connectivity may be susceptible to artifacts induced...
59 citations
TL;DR: This study highlights an effect of modularity on collaboration that previously has been overlooked, and shows how firms need to complement modular designs with integrating practices that stimulate cooperation.
53 citations
TL;DR: This study offers a first investigation of the effects of testing integration and modularity within a configuration of commonly superimposed landmarks using some of the most widely employed statistical methods available to this aim.
Abstract: Studies of morphological integration and modularity are a hot topic in evolutionary developmental biology. Geometric morphometrics using Procrustes methods offers powerful tools to quantitatively investigate morphological variation and, within this methodological framework, a number of different methods has been put forward to test if different regions within an anatomical structure behave like modules or, vice versa, are highly integrated and covary strongly. Although some exploratory techniques do not require a priori modules, commonly modules are specified in advance based on prior knowledge. Once this is done, most of the methods can be applied either by subdividing modules and performing separate Procrustes alignments or by splitting shape coordinates of anatomical landmarks into modules after a common superimposition. This second approach is particularly interesting because, contrary to completely separate blocks analyses, it preserves information on relative size and position of the putative modules. However, it also violates one of the fundamental assumptions on which Procrustes methods are based, which is that one should not analyse or interpret subsets of landmarks from a common superimposition, because the choice of that superimposition is purely based on statistical convenience (although with sound theoretical foundations) and not on a biological model of variance and covariance. In this study, I offer a first investigation of the effects of testing integration and modularity within a configuration of commonly superimposed landmarks using some of the most widely employed statistical methods available to this aim. When applied to simulated shapes with random non-modular isotropic variation, standard methods frequently recovered significant but arbitrary patterns of integration and modularity. Re-superimposing landmarks within each module, before testing integration or modularity, generally removes this artifact. The study, although preliminary and exploratory in nature, raises an important issue and indicates an avenue for future research. It also suggests that great caution should be exercised in the application and interpretation of findings from analyses of modularity and integration using Procrustes shape data, and that issues might be even more serious using some of the most common methods for handling the increasing popular semilandmark data used to analyse 2D outlines and 3D surfaces.
TL;DR: A novel modularity-based discrete state transition algorithm (MDSTA) is proposed to obtain more optimal and stable solutions for community detection in networks and based on the heuristic information of the network, vertex substitute transformationoperator and community substitute transformation operator are proposed for global search.
TL;DR: This study explores the modularity equation between overlap (grouping) functions and overlap functions, grouping functions, uninorms and nullnorms and generalizes the known ones about modularity for certain associative operators.
TL;DR: For Yeast and Human PPI networks, Louvain method is likely the best method to find communities in terms of detecting known core pathways in a reasonable time, and is the fastest method for community detection.
Abstract: Community detection algorithms are fundamental tools to uncover important features in networks. There are several studies focused on social networks but only a few deal with biological networks. Directly or indirectly, most of the methods maximize modularity, a measure of the density of links within communities as compared to links between communities. Here we analyze six different community detection algorithms, namely, Combo, Conclude, Fast Greedy, Leading Eigen, Louvain and Spinglass, on two important biological networks to find their communities and evaluate the results in terms of topological and functional features through Kyoto Encyclopedia of Genes and Genomes pathway and Gene Ontology term enrichment analysis. At a high level, the main assessment criteria are 1) appropriate community size (neither too small nor too large), 2) representation within the community of only one or two broad biological functions, 3) most genes from the network belonging to a pathway should also belong to only one or two communities, and 4) performance speed. The first network in this study is a network of Protein-Protein Interactions (PPI) in Saccharomyces cerevisiae (Yeast) with 6532 nodes and 229,696 edges and the second is a network of PPI in Homo sapiens (Human) with 20,644 nodes and 241,008 edges. All six methods perform well, i.e., find reasonably sized and biologically interpretable communities, for the Yeast PPI network but the Conclude method does not find reasonably sized communities for the Human PPI network. Louvain method maximizes modularity by using an agglomerative approach, and is the fastest method for community detection. For the Yeast PPI network, the results of Spinglass method are most similar to the results of Louvain method with regard to the size of communities and core pathways they identify, whereas for the Human PPI network, Combo and Spinglass methods yield the most similar results, with Louvain being the next closest. For Yeast and Human PPI networks, Louvain method is likely the best method to find communities in terms of detecting known core pathways in a reasonable time.
TL;DR: A novel multi-objective Bat Algorithm that uses Mean Shift algorithm to generate the initial population, to obtain solutions of high quality and simultaneously optimizes the modularity density and the normalized mutual information of the solutions as objective functions is proposed.
Abstract: Many evolutionary algorithms have been proposed to deal with the problem of community detection in social dynamic networks. Some algorithms need to fix parameters in advance; others use a random process to generate the initial population and to apply the algorithm operators. These drawbacks increase the search space and cause a high spatial and temporary complexity. To overcome these weaknesses, we propose in this paper a novel multi-objective Bat Algorithm that uses Mean Shift algorithm to generate the initial population, to obtain solutions of high quality. In our proposal, Bat Algorithm simultaneously optimizes the modularity density and the normalized mutual information of the solutions as objective functions. The operators of the algorithm are applied to the problem of community detection in social dynamic networks by giving another sense to the velocity, frequency, loudness and the pulse rate of natural Bat. The algorithm keeps the principal of the Mean Shift algorithm to generate new solution and avoid the random process by defining a new mutation operator. The algorithm does not need to the non-dominated sorted approach or the crowding distance, but it attributes a weight to each objective function. The method is tested on artificial and real dynamic networks and the experiments show satisfactory results in terms of normalized mutual information, modularity and error rate.
TL;DR: It is shown that it is important to identify more compact modules for better performance, and it is demonstrated that “overlapping” community detection algorithms should be the preferred choice for disease module identification since several genes participate in multiple biological functions.
Abstract: Biological networks catalog the complex web of interactions happening between different molecules, typically proteins, within a cell. These networks are known to be highly modular, with groups of proteins associated with specific biological functions. Human diseases often arise from the dysfunction of one or more such proteins of the biological functional group. The ability, to identify and automatically extract these modules has implications for understanding the etiology of different diseases as well as the functional roles of different protein modules in disease. The recent DREAM challenge posed the problem of identifying disease modules from six heterogeneous networks of proteins/genes. There exist many community detection algorithms, but all of them are not adaptable to the biological context, as these networks are densely connected and the size of biologically relevant modules is quite small. The contribution of this study is 3-fold: first, we present a comprehensive assessment of many classic community detection algorithms for biological networks to identify non-overlapping communities, and propose heuristics to identify small and structurally well-defined communities-core modules. We evaluated our performance over 180 GWAS datasets. In comparison to traditional approaches, with our proposed approach we could identify 50% more number of disease-relevant modules. Thus, we show that it is important to identify more compact modules for better performance. Next, we sought to understand the peculiar characteristics of disease-enriched modules and what causes standard community detection algorithms to detect so few of them. We performed a comprehensive analysis of the interaction patterns of known disease genes to understand the structure of disease modules and show that merely considering the known disease genes set as a module does not give good quality clusters, as measured by typical metrics such as modularity and conductance. We go on to present a methodology leveraging these known disease genes, to also include the neighboring nodes of these genes into a module, to form good quality clusters and subsequently extract a "gold-standard set" of disease modules. Lastly, we demonstrate, with justification, that "overlapping" community detection algorithms should be the preferred choice for disease module identification since several genes participate in multiple biological functions.
TL;DR: The analysis clearly shows the superiority of the traveling-wave systems over the mono-mode and multi-mode cavity-based systems when it comes to the design and application of microwave flow reactors at relevant production scales.
Abstract: The paper discusses the currents status and future perspectives of the utilization of microwaves, as a selective and locally controlled heating method, in heterogeneous catalytic flow reactors. Various factors related to the microwave-catalyst interaction and the design of microwave-assisted catalytic reactor systems are analyzed. The analysis clearly shows the superiority of the traveling-wave systems over the mono-mode and multi-mode cavity-based systems when it comes to the design and application of microwave flow reactors at relevant production scales.
TL;DR: It is found that modular structure necessarily causes a loss of information, effectively silencing the input from a fraction of the group, and this suggests that in naturalistic environments containing correlated information, large animal groups may be able to exploit modular structure to improve decision accuracy while retaining other benefits of large group size.
Abstract: Many animal groups exhibit signatures of persistent internal modular structure, whereby individuals consistently interact with certain groupmates more than others. In such groups, information relevant to a collective decision may spread unevenly through the group, but how this impacts the quality of the resulting decision is not well understood. Here, we explicitly model modularity within animal groups and examine how it affects the amount of information represented in collective decisions, as well as the accuracy of those decisions. We find that modular structure necessarily causes a loss of information, effectively silencing the input from a fraction of the group. However, the effect of this information loss on collective accuracy depends on the informational environment in which the decision is made. In simple environments, the information loss is detrimental to collective accuracy. By contrast, in complex environments, modularity tends to improve accuracy. This is because small group sizes typically maximize collective accuracy in such environments, and modular structure allows a large group to behave like a smaller group (in terms of its decision-making). These results suggest that in naturalistic environments containing correlated information, large animal groups may be able to exploit modular structure to improve decision accuracy while retaining other benefits of large group size. This article is part of the theme issue 'Liquid brains, solid brains: How distributed cognitive architectures process information'.
TL;DR: The final of the BBC Sports Personality of the Year 2016 was won by Emma Sherratt, while David J. Gower and Carla Bardua finished second and third respectively.
Abstract: Ashleigh F. Marshall, Carla Bardua, David J. Gower, Mark Wilkinson, Emma Sherratt and Anjali Goswami
02 May 2019
TL;DR: It is demonstrated that there exists an optimal modularity for memory performance, where a balance between local cohesion and global connectivity is established, allowing optimally modular networks to remember longer.
Abstract: The neural network is a powerful computing framework that has been exploited by biological evolution and by humans for solving diverse problems. Although the computational capabilities of neural ne...
TL;DR: This study presents a large-scale design (or meta-panel) adopting modularity for customizable acoustical performance that not only could address the potential manufacturing issues of large- scale membrane-type acoustic metamaterials but also could provide customizable acoustic performance, demonstrating scalability and modularity.
TL;DR: The concept of “cyber-physical modeling and simulation (CPMS)” is proposed which enables to constitute cyber-physical systems (CPS) with scalable complexity, modularity and variability in size.
TL;DR: This research proposes a new integer linear programming model to detect community structure in real-life networks and also identifies the most influential node within each community and demonstrates that in most cases the proposed integer programming model performs better than the existing optimization model with respect to modularity, Silhouette coefficient and computational time.
Abstract: Integer programming models for community detection in relational networks have diverse applications in different fields. From making our lives easier by improving search engine optimization to saving our lives by aiding in threat detection and disaster management, researches in this niche have added value to human experience and knowledge. Besides the community structure, the influential nodes or members in a complex network are highly effective at diffusing information quickly to others in the community. Prior research dealing with the use of optimization models for clustering networks has independently focused on detecting communities. In this research, we propose a new integer linear programming model to detect community structure in real-life networks and also identify the most influential node within each community. We validate the proposed model by testing it on a well-established community network. Further, the performance of the proposed model is evaluated by comparing it with the existing best performing optimization model as well as three heuristic approaches for community detection. The experimental results indicate that in most cases the proposed integer programming model performs better than the existing optimization model with respect to modularity, Silhouette coefficient and computational time. Besides, our model yields superior Silhouette and competitive modularity values compared to the heuristic approaches in many cases.
TL;DR: Results show that the topological examination of SC networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD.
Abstract: Alzheimer's disease (AD) causes the progressive deterioration of neural connections, disrupting structural connectivity (SC) networks within the brain. Graph-based analyses of SC networks have shown that topological properties can reveal the course of AD propagation. Different whole-brain parcellation schemes have been developed to define the nodes of these SC networks, although it remains unclear which scheme can best describe the AD-related deterioration of SC networks. In this study, four whole-brain parcellation schemes with different numbers of parcels were used to define SC network nodes. SC networks were constructed based on high angular resolution diffusion imaging (HARDI) tractography for a mixed cohort that includes 20 normal controls (NC), 20 early mild cognitive impairment (EMCI), 20 late mild cognitive impairment (LMCI), and 20 AD patients, from the Alzheimer's Disease Neuroimaging Initiative. Parcellation schemes investigated in this study include the OASIS-TRT-20 (62 regions), AAL (116 regions), HCP-MMP (180 regions), and Gordon-rsfMRI (333 regions), which have all been widely used for the construction of brain structural or functional connectivity networks. Topological characteristics of the SC networks, including the network strength, global efficiency, clustering coefficient, rich-club, characteristic path length, k-core, rich-club coefficient, and modularity, were fully investigated at the network level. Statistical analyses were performed on these metrics using Kruskal-Wallis tests to examine the group differences that were apparent at different stages of AD progression. Results suggest that the HCP-MMP scheme is the most robust and sensitive to AD progression, while the OASIS-TRT-20 scheme is sensitive to group differences in network strength, global efficiency, k-core, and rich-club coefficient at k-levels from 18 and 39. With the exception of the rich-club and modularity coefficients, AAL could not significantly identify group differences on other topological metrics. Further, the Gordon-rsfMRI atlas only significantly differentiates the groups on network strength, characteristic path length, k-core, and rich-club coefficient. Results show that the topological examination of SC networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD.
TL;DR: In this paper, the authors discuss modularity and the use of modularity maximization as the basis for community detection, and introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network.
Abstract: Community structure is one of the most relevant features encountered in numerous real-world applications of networked systems. Despite the tremendous effort of scientists working on this subject over the past few decades to characterize, model, and analyze communities, more investigations are needed to better understand the impact of community structure and its dynamics on networked systems. Here, we first focus on generative models of communities in complex networks and their role in developing strong foundation for community detection algorithms. We discuss modularity and the use of modularity maximization as the basis for community detection. Then, we overview the Stochastic Block Model, its different variants, and inference of community structures from such models. Next, we focus on time evolving networks, where existing nodes and links can disappear and/or new nodes and links may be introduced. The extraction of communities under such circumstances poses an interesting and non-trivial problem that has gained considerable interest over the last decade. We briefly discuss considerable advances made in this field recently. Finally, we focus on immunization strategies essential for targeting the influential spreaders of epidemics in modular networks. Their main goal is to select and immunize a small proportion of individuals from the whole network to control the diffusion process. Various strategies have emerged over the years suggesting different ways to immunize nodes in networks with overlapping and non-overlapping community structure. We first discuss stochastic strategies that require little or no information about the network topology at the expense of their performance. Then, we introduce deterministic strategies that have proven to be very efficient in controlling the epidemic outbreaks, but require complete knowledge of the network.
TL;DR: In this article, the authors quantified brain network modularity in young adults who underwent cognitive training with casual video games that engaged working memory and reasoning processes and found that a more modular brain network organization may allow for greater training responsiveness.
TL;DR: In this article, the authors study the community structure of industrial SAT instances and show that most application benchmarks are characterized by a high modularity, whereas random SAT instances are closer to the classical Erdos-Renyi random graph model, where no structure can be observed.
Abstract: Modern SAT solvers have experienced a remarkable progress on solving industrial instances. Most of the techniques have been developed after an intensive experimental process. It is believed that these techniques exploit the underlying structure of industrial instances. However, there are few works trying to exactly characterize the main features of this structure.
The research community on complex networks has developed techniques of analysis and algorithms to study real-world graphs that can be used by the SAT community. Recently, there have been some attempts to analyze the structure of industrial SAT instances in terms of complex networks, with the aim of explaining the success of SAT solving techniques, and possibly improving them.
In this paper, inspired by the results on complex networks, we study the community structure, or modularity, of industrial SAT instances. In a graph with clear community structure, or high modularity, we can find a partition of its nodes into communities such that most edges connect variables of the same community. In our analysis, we represent SAT instances as graphs, and we show that most application benchmarks are characterized by a high modularity. On the contrary, random SAT instances are closer to the classical Erdos-Renyi random graph model, where no structure can be observed. We also analyze how this structure evolves by the effects of the execution of a CDCL SAT solver. In particular, we use the community structure to detect that new clauses learned by the solver during the search contribute to destroy the original structure of the formula. This is, learned clauses tend to contain variables of distinct communities.
TL;DR: A model that simulates the evolution of consumer species using resource species following simple rules derived from the integrative hypothesis of specialization (IHS) demonstrates that resource heterogeneity drives network topology and suggests that networks containing similar species differ from heterogeneous networks and that modules may not present the topology of entire networks.
Abstract: Nestedness and modularity have been recurrently observed in species interaction networks. Some studies argue that those topologies result from selection against unstable networks, and others propose that they likely emerge from processes driving the interactions between pairs of species. Here we present a model that simulates the evolution of consumer species using resource species following simple rules derived from the integrative hypothesis of specialization (IHS). Without any selection on stability, our model reproduced all commonly observed network topologies. Our simulations demonstrate that resource heterogeneity drives network topology. On the one hand, systems containing only homogeneous resources form generalized nested networks, in which generalist consumers have higher performance on each resource than specialists. On the other hand, heterogeneous systems tend to have a compound topology: modular with internally nested modules, in which generalists that divide their interactions between modules have low performance. Our results demonstrate that all real-world topologies likely emerge through processes driving interactions between pairs of species. Additionally, our simulations suggest that networks containing similar species differ from heterogeneous networks and that modules may not present the topology of entire networks.
TL;DR: A community detection method by improved label propagation and fuzzy C-means and the results showed that this method can achieve better accuracy on synthetic and real network.
Abstract: Community detection algorithms have important significance in the research and practical application of complex network theory. This paper proposes a community detection method by improved label propagation and fuzzy C-means. Due to low accuracy and instability detection results, we modify original label propagation framework. Primarily, initial labels of vertexes are assigned by neighbor evaluation method. Secondarily, the labels of vertexes with large diversity in each community are revised by fuzzy C-means membership vectors. Tertiarily, parameters are updated until communities status is stabilized ultimately. The results showed that this method can achieve better accuracy on synthetic and real network.