Iables, which are independent from the heritability estimates of either trait, and indicate the degree to which they share genetic influences .e., common genes. A “correlated factors” solution then estimates common A, C and E influences, and thus allows phenotypic correlations to be decomposed into these sources of covariance (as in Fig. 2 and Supplementary Tables S16 19). Alternatively, the (algebraically equivalent) Cholesky decomposition focuses instead on the total influences on each trait in sequence, and determines at each step the proportion of its A, C and E components that are shared with, or independent from, each variable. This process is analogous to stepwise multiple regression, accounting for the influences on each variable in turn, in order to determine the residual portions at each stage. Thus in bivariate models (as in Fig. 3 and Supplementary Tables S20 23), path estimates show the proportion of each componentScientific RepoRts | 6:30545 | DOI: 10.1038/srepwww.nature.com/buy MK-1439 scientificreports/that is common to both variables, and the proportion unique to the second variable. Similarly, trivariate and further extensions (as in Supplementary Figs S1 and S2 and Supplementary Tables S29 36) indicate the influences in common to all variables, then those common to all but the first, and so on, and finally those influences unique to the last variable.
www.nature.com/scientificreportsOPENA Comparative Analysis of Community Detection Algorithms on Artificial NetworksZhao Yang, Ren?Algesheimer Claudio J. TessoneMany community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time. Relationships between constituents of complex systems (be it in nature, society, or technological applications) can be represented in terms of networks. In this portrayal, the elements composing the system are described as nodes and their interactions as links. At the global level, the topology of these interactions ?far from being trivial ?is in itself of complex 4-Hydroxytamoxifen site nature1,2. Importantly, these networks further display some level of organisation at an intermediate scale. At this mesoscopic level, it is possible to identify groups of nodes that are heavi.Iables, which are independent from the heritability estimates of either trait, and indicate the degree to which they share genetic influences .e., common genes. A “correlated factors” solution then estimates common A, C and E influences, and thus allows phenotypic correlations to be decomposed into these sources of covariance (as in Fig. 2 and Supplementary Tables S16 19). Alternatively, the (algebraically equivalent) Cholesky decomposition focuses instead on the total influences on each trait in sequence, and determines at each step the proportion of its A, C and E components that are shared with, or independent from, each variable. This process is analogous to stepwise multiple regression, accounting for the influences on each variable in turn, in order to determine the residual portions at each stage. Thus in bivariate models (as in Fig. 3 and Supplementary Tables S20 23), path estimates show the proportion of each componentScientific RepoRts | 6:30545 | DOI: 10.1038/srepwww.nature.com/scientificreports/that is common to both variables, and the proportion unique to the second variable. Similarly, trivariate and further extensions (as in Supplementary Figs S1 and S2 and Supplementary Tables S29 36) indicate the influences in common to all variables, then those common to all but the first, and so on, and finally those influences unique to the last variable.
www.nature.com/scientificreportsOPENA Comparative Analysis of Community Detection Algorithms on Artificial NetworksZhao Yang, Ren?Algesheimer Claudio J. TessoneMany community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time. Relationships between constituents of complex systems (be it in nature, society, or technological applications) can be represented in terms of networks. In this portrayal, the elements composing the system are described as nodes and their interactions as links. At the global level, the topology of these interactions ?far from being trivial ?is in itself of complex nature1,2. Importantly, these networks further display some level of organisation at an intermediate scale. At this mesoscopic level, it is possible to identify groups of nodes that are heavi.
