The Experts below are selected from a list of 447528 Experts worldwide ranked by ideXlab platform
Hongyu Xiang - One of the best experts on this subject based on the ideXlab platform.
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effects of heavy metals and soil physicochemical properties on wetland soil microbial biomass and bacterial Community Structure
Science of The Total Environment, 2016Co-Authors: Chang Zhang, Haibing Xiao, Linjing Deng, Haipeng Wu, Yujie Yuan, Guangming Zeng, Jie Liang, Hongyu XiangAbstract:Heavy metals (HMs) contamination is a serious environmental issue in wetland soil. Understanding the micro ecological characteristic of HMs polluted wetland soil has become a public concern. The goal of this study was to identify the effects of HMs and soil physicochemical properties on soil microorganisms and prioritize some parameters that contributed significantly to soil microbial biomass (SMB) and bacterial Community Structure. Bacterial Community Structure was analyzed by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). Relationships between soil environment and microorganisms were analyzed by correlation analysis and redundancy analysis (RDA). The result indicated relationship between SMB and HMs was weaker than SMB and physicochemical properties. The RDA showed all eight parameters explained 74.9% of the variation in the bacterial DGGE profiles. 43.4% (contain the variation shared by Cr, Cd, Pb and Cu) of the variation for bacteria was explained by the four kinds of HMs, demonstrating HMs contamination had a significant influence on the changes of bacterial Community Structure. Cr solely explained 19.4% (p<0.05) of the variation for bacterial Community Structure, and Cd explained 17.5% (p<0.05), indicating Cr and Cd were the major factors related to bacterial Community Structure changes.
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effects of heavy metals and soil physicochemical properties on wetland soil microbial biomass and bacterial Community Structure
Science of The Total Environment, 2016Co-Authors: Chang Zhang, Haibing Xiao, Linjing Deng, Haipeng Wu, Yujie Yuan, Guangming Zeng, Jie Liang, Hongyu XiangAbstract:Heavy metals (HMs) contamination is a serious environmental issue in wetland soil. Understanding the micro ecological characteristic of HMs polluted wetland soil has become a public concern. The goal of this study was to identify the effects of HMs and soil physicochemical properties on soil microorganisms and prioritize some parameters that contributed significantly to soil microbial biomass (SMB) and bacterial Community Structure. Bacterial Community Structure was analyzed by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). Relationships between soil environment and microorganisms were analyzed by correlation analysis and redundancy analysis (RDA). The result indicated relationship between SMB and HMs was weaker than SMB and physicochemical properties. The RDA showed all eight parameters explained 74.9% of the variation in the bacterial DGGE profiles. 43.4% (contain the variation shared by Cr, Cd, Pb and Cu) of the variation for bacteria was explained by the four kinds of HMs, demonstrating HMs contamination had a significant influence on the changes of bacterial Community Structure. Cr solely explained 19.4% (p<0.05) of the variation for bacterial Community Structure, and Cd explained 17.5% (p<0.05), indicating Cr and Cd were the major factors related to bacterial Community Structure changes.
Xiangsun Zhang - One of the best experts on this subject based on the ideXlab platform.
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analysis of stability of Community Structure across multiple hierarchical levels
EPL, 2013Co-Authors: Xiangsun ZhangAbstract:The analysis of stability of Community Structure is an important problem for scientists from many fields. Here, we propose a new framework to reveal hidden properties of Community Structures by quantitatively analyzing the dynamics of the Potts model. Specifically we model the Potts procedure of Community Structure detection by a Markov process, which has a clear mathematical explanation. Critical topological information regarding multivariate spin configuration could also be inferred from the spectral significance of the Markov process. We test our framework on some example networks and find it does not have resolution limitation problems at all. Results have shown the model we proposed is able to uncover the hierarchical Structure in different scales effectively and efficiently.
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potts model based on a markov process computation solves the Community Structure problem effectively
Physical Review E, 2012Co-Authors: Huijia Li, Lingyun Wu, Junhua Zhang, Yong Wang, Xiangsun ZhangAbstract:: The Potts model is a powerful tool to uncover Community Structure in complex networks. Here, we propose a framework to reveal the optimal number of communities and stability of network Structure by quantitatively analyzing the dynamics of the Potts model. Specifically we model the Community Structure detection Potts procedure by a Markov process, which has a clear mathematical explanation. Then we show that the local uniform behavior of spin values across multiple timescales in the representation of the Markov variables could naturally reveal the network's hierarchical Community Structure. In addition, critical topological information regarding multivariate spin configuration could also be inferred from the spectral signatures of the Markov process. Finally an algorithm is developed to determine fuzzy communities based on the optimal number of communities and the stability across multiple timescales. The effectiveness and efficiency of our algorithm are theoretically analyzed as well as experimentally validated.
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Community Structure detection based on potts model and network s spectral characterization
EPL, 2012Co-Authors: Yong Wang, Zhiping Liu, Luonan Chen, Xiangsun ZhangAbstract:The Potts model was used to uncover Community Structure in complex networks. However, it could not reveal much important information such as the optimal number of communities and the overlapping nodes hidden in networks effectively. Differently from the previous studies, we established a new framework to study the dynamics of Potts model for Community Structure detection by using the Markov process, which has a clear mathematic explanation. Based on our framework, we showed that the local uniform behavior of spin values could naturally reveal the hierarchical Community Structure of a given network. Critical topological information regarding the optimal Community Structure could also be inferred from spectral signatures of the Markov process. A two-stage algorithm to detect Community Structure is developed. The effectiveness and efficiency of the algorithm has been theoretically analyzed as well as experimentally validated.
Chang Zhang - One of the best experts on this subject based on the ideXlab platform.
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effects of heavy metals and soil physicochemical properties on wetland soil microbial biomass and bacterial Community Structure
Science of The Total Environment, 2016Co-Authors: Chang Zhang, Haibing Xiao, Linjing Deng, Haipeng Wu, Yujie Yuan, Guangming Zeng, Jie Liang, Hongyu XiangAbstract:Heavy metals (HMs) contamination is a serious environmental issue in wetland soil. Understanding the micro ecological characteristic of HMs polluted wetland soil has become a public concern. The goal of this study was to identify the effects of HMs and soil physicochemical properties on soil microorganisms and prioritize some parameters that contributed significantly to soil microbial biomass (SMB) and bacterial Community Structure. Bacterial Community Structure was analyzed by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). Relationships between soil environment and microorganisms were analyzed by correlation analysis and redundancy analysis (RDA). The result indicated relationship between SMB and HMs was weaker than SMB and physicochemical properties. The RDA showed all eight parameters explained 74.9% of the variation in the bacterial DGGE profiles. 43.4% (contain the variation shared by Cr, Cd, Pb and Cu) of the variation for bacteria was explained by the four kinds of HMs, demonstrating HMs contamination had a significant influence on the changes of bacterial Community Structure. Cr solely explained 19.4% (p<0.05) of the variation for bacterial Community Structure, and Cd explained 17.5% (p<0.05), indicating Cr and Cd were the major factors related to bacterial Community Structure changes.
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effects of heavy metals and soil physicochemical properties on wetland soil microbial biomass and bacterial Community Structure
Science of The Total Environment, 2016Co-Authors: Chang Zhang, Haibing Xiao, Linjing Deng, Haipeng Wu, Yujie Yuan, Guangming Zeng, Jie Liang, Hongyu XiangAbstract:Heavy metals (HMs) contamination is a serious environmental issue in wetland soil. Understanding the micro ecological characteristic of HMs polluted wetland soil has become a public concern. The goal of this study was to identify the effects of HMs and soil physicochemical properties on soil microorganisms and prioritize some parameters that contributed significantly to soil microbial biomass (SMB) and bacterial Community Structure. Bacterial Community Structure was analyzed by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE). Relationships between soil environment and microorganisms were analyzed by correlation analysis and redundancy analysis (RDA). The result indicated relationship between SMB and HMs was weaker than SMB and physicochemical properties. The RDA showed all eight parameters explained 74.9% of the variation in the bacterial DGGE profiles. 43.4% (contain the variation shared by Cr, Cd, Pb and Cu) of the variation for bacteria was explained by the four kinds of HMs, demonstrating HMs contamination had a significant influence on the changes of bacterial Community Structure. Cr solely explained 19.4% (p<0.05) of the variation for bacterial Community Structure, and Cd explained 17.5% (p<0.05), indicating Cr and Cd were the major factors related to bacterial Community Structure changes.
M. E. J. Newman - One of the best experts on this subject based on the ideXlab platform.
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graph spectra and the detectability of Community Structure in networks
Physical Review Letters, 2012Co-Authors: Raj Rao Nadakuditi, M. E. J. NewmanAbstract:We study networks that display Community Structure--groups of nodes within which connections are unusually dense. Using methods from random matrix theory, we calculate the spectra of such networks in the limit of large size, and hence demonstrate the presence of a phase transition in matrix methods for Community detection, such as the popular modularity maximization method. The transition separates a regime in which such methods successfully detect the Community Structure from one in which the Structure is present but is not detected. By comparing these results with recent analyses of maximum-likelihood methods, we are able to show that spectral modularity maximization is an optimal detection method in the sense that no other method will succeed in the regime where the modularity method fails.
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Finding and evaluating Community Structure in networks
Physical Review E, 2004Co-Authors: M. Newman, M. E. J. Newman, Michelle GirvanAbstract:We propose and study a set of algorithms for discovering Community Structure in networks—natural divisions of network nodes into densely connected subgroups. Our algorithms all share two definitive features: first, they involve iterative removal of edges from the network to split it into communities, the edges removed being identified using any one of a number of possible “betweenness” measures, and second, these measures are, crucially, recalculated after each removal. We also propose a measure for the strength of the Community Structure found by our algorithms, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our algorithms are highly effective at discovering Community Structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes dauntingly complex Structure of networked systems.
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Community Structure in social and biological networks
Proceedings of the National Academy of Sciences, 2002Co-Authors: Michelle Girvan, M. E. J. NewmanAbstract:A number of recent studies have focused on the statistical properties of networked systems such as social networks and the Worldwide Web. Researchers have concentrated particularly on a few properties that seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this article, we highlight another property that is found in many networks, the property of Community Structure, in which network nodes are joined together in tightly knit groups, between which there are only looser connections. We propose a method for detecting such communities, built around the idea of using centrality indices to find Community boundaries. We test our method on computer-generated and real-world graphs whose Community Structure is already known and find that the method detects this known Structure with high sensitivity and reliability. We also apply the method to two networks whose Community Structure is not well known--a collaboration network and a food web--and find that it detects significant and informative Community divisions in both cases.
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Community Structure in social and biological networks
2001Co-Authors: Michelle Girvan, M. E. J. NewmanAbstract:A number of recent studies have focused on the statistical properties of networked systems such as social networks and the World-Wide Web. Researchers have concentrated particularly on a few properties which seem to be common to many networks: the small-world property, power-law degree distributions, and network transitivity. In this paper, we highlight another property which is found in many networks, the property of Community Structure, in which network nodes are joined together in tightly-knit groups between which there are only looser connections. We propose a new method for detecting such communities, built around the idea of using centrality indices to find Community boundaries. We test our method on computer generated and real-world graphs whose Community Structure is already known, and find that it detects this known Structure with high sensitivity and reliability. We also apply the method to two networks whose Community Structure is not well-known - a collaboration network and a food web - and find that it detects significant and informative Community divisions in both cases.
Santo Fortunato - One of the best experts on this subject based on the ideXlab platform.
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characterizing the Community Structure of complex networks
PLOS ONE, 2010Co-Authors: Andrea Lancichinetti, Mikko Kivela, Jari Saramaki, Santo FortunatoAbstract:Background Community Structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of Community detection, very little attention has been so far devoted to the investigation of communities in real networks.
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detecting the overlapping and hierarchical Community Structure in complex networks
New Journal of Physics, 2009Co-Authors: Andrea Lancichinetti, Santo Fortunato, Janos KerteszAbstract:Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this Community Structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical Structure. The method is based on the local optimization of a fitness function. Community Structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.
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detecting the overlapping and hierarchical Community Structure of complex networks
arXiv: Physics and Society, 2008Co-Authors: Andrea Lancichinetti, Santo Fortunato, Janos KerteszAbstract:Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this Community Structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here we present the first algorithm that finds both overlapping communities and the hierarchical Structure. The method is based on the local optimization of a fitness function. Community Structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling to investigate different hierarchical levels of organization. Tests on real and artificial networks give excellent results.
