community detection in graphs using singular value decomposition a spectral algorithm for community detection is presented the algorithm consists of three stages 1 matrix factorization of two matrix forms square signless laplacian for unipartite graphs and rectangular adjacency matrix for bipartite graphs using singular value decompostion svd 2 dimensionality reduction using an optimal linear approximation and 3 clustering vertices using dot products in reduced dimensional space the algorithm reveals communities in graphs without placing any restriction on the input network type or the output community type it is applicable on unipartite or bipartite unweighted or weighted networks it places no requirement on strict community membership and automatically reveals the number of clusters their respective sizes and overlaps and hierarchical modular organization by representing vertices as vectors in real space expressed as linear combinations of the orthogonal bases described by svd orthogonality becomes the metric for classifying vertices into communities results on several test and real world networks are presented including cases where a mix of disjointed overlapping or hierarchical communities are known to exist in the network

benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities many complex networks display a mesoscopic structure with groups of nodes sharing many links with the other nodes in their group and comparatively few with nodes of different groups this feature is known as community structure and encodes precious information about the organization and the function of the nodes many algorithms have been proposed but it is not yet clear how they should be tested recently we have proposed a general class of undirected and unweighted benchmark graphs with heterogeneous distributions of node degree and community size an increasing attention has been recently devoted to develop algorithms able to consider the direction and the weight of the links which require suitable benchmark graphs for testing in this paper we extend the basic ideas behind our previous benchmark to generate directed and weighted networks with built in community structure we also consider the possibility that nodes belong to more communities a feature occurring in real systems such as social networks as a practical application we show how modularity optimization performs on our benchmark

detect overlapping and hierarchical community structure in networks abstract clustering and community structure is crucial for many network systems and the related dynamic processes it has been shown that communities are usually overlapping and hierarchical however previous methods investigate these two properties of community structure separately this paper proposes an algorithm eagle to detect both the overlapping and hierarchical properties of complex community structure together this algorithm deals with the set of maximal cliques and adopts an agglomerative framework the quality function of modularity is extended to evaluate the goodness of a cover the examples of application to real world networks give excellent results

adaptive tracking control for switched nonlinear systems with fuzzy actuator backlash abstract this paper concentrates on the adaptive tracking control problem for a class of switched nonlinear systems in nonstrict feedback form with uncertain actuator backlash the switching signal considered in our model is arbitrary the adaptive tracking controller design procedure is divided into three parts firstly a novel fuzzy backlash model is presented to describe the uncertain backlash then the fuzzy backlash is defuzzified and approximated by the linear combination of a linear term and a disturbance like term without constructing a backlash inverse finally an adaptive backstepping based tracking controller via common lyapunov function clf method is presented to guarantee the tracking error asymptotically converges to an adjustable region of zero the stability analysis and simulation examples are presented at the end of this paper to further verify effectiveness of our control scheme

finding community structure in networks using the eigenvectors of matrices we consider the problem of detecting communities or modules in networks groups of vertices with a higher than average density of edges connecting them previous work indicates that a robust approach to this problem is the maximization of the benefit function known as modularity over possible divisions of a network here we show that this maximization process can be written in terms of the eigenspectrum of a matrix we call the modularity matrix which plays a role in community detection similar to that played by the graph laplacian in graph partitioning calculations this result leads us to a number of possible algorithms for detecting community structure as well as several other results including a spectral measure of bipartite structure in networks and a centrality measure that identifies vertices that occupy central positions within the communities to which they belong the algorithms and measures proposed are illustrated with applications to a variety of real world complex networks

modeling and parameter identification of systems with multisegment piecewise linear characteristics this paper deals with the modeling and parameter identification of nonlinear systems having multi segment piecewise linear characteristics the decomposition of the corresponding mapping provides a new form of multi segment nonlinearity representation leading to an output equation where all the parameters to be estimated are separated hence an iterative method with internal variable estimation can be applied for parameter identification using input output data records the only required a priori knowledge of the nonlinear characteristic represents the limits for the domain partition the proposed model of given static nonlinearity is also incorporated into the hammerstein model examples of parameter identification for static and dynamic systems with multi segment piecewise linear characteristics are presented

brief paper modeling and identification of systems with backlash a new analytic form of backlash characteristic description is introduced which uses appropriate switching functions and their complements the backlash parameters in the model equation are separated hence their estimation can be solved as a quasi linear problem using an iterative method with internal variable estimation also the identification of cascaded systems consisting of an input backlash followed by a linear dynamic system is presented simulation studies of backlash and cascaded systems identification are included

modularity and community detection in bipartite networks the modularity of a network quantifies the extent relative to a null model network to which vertices cluster into community groups we define a null model appropriate for bipartite networks and use it to define a bipartite modularity the bipartite modularity is presented in terms of a modularity matrix b some key properties of the eigenspectrum of b are identified and used to describe an algorithm for identifying modules in bipartite networks the algorithm is based on the idea that the modules in the two parts of the network are dependent with each part mutually being used to induce the vertices for the other part into the modules we apply the algorithm to real world network data showing that the algorithm successfully identifies the modular structure of bipartite networks

a new deterministic identification approach to hammerstein systems the deterministic identification of hammerstein systems is investigated in this paper based on the over sampling technique a new deterministic identification approach is presented which blindly identifies the linear dynamic part followed by the estimation of the nonlinear function the proposed method allows us to identify the hammerstein system using an over sampling rate smaller than the numerator polynomial s length of the linear dynamic part as required by other existing methods in addition it can obtain the true values of the system parameters in the noise free case and an asymptotically consistent estimate in the presence of noise the richness condition of the system input and the selection of the over sampling rate are studied for the identifiability of the hammerstein system simulation examples are given to show the performance of the proposed method

detection of community structure in networks based on community coefficients abstract determining community structure in networks is fundamental to the analysis of the structural and functional properties of those networks including social networks computer networks and biological networks modularity function q which was proposed by newman and girvan was once the most widely used criterion for evaluating the partition of a network into communities however modularity q is subject to a serious resolution limit in this paper we propose a new function for evaluating the partition of a network into communities this is called community coefficient c using community coefficient c we can automatically identify the ideal number of communities in the network without any prior knowledge we demonstrate that community coefficient c is superior to the modularity q and does not have a resolution limit we also compared the two widely used community structure partitioning methods the hierarchical partitioning algorithm and the normalized cuts ncut spectral partitioning algorithm we tested these methods on computer generated networks and real world networks whose community structures were already known the ncut algorithm and community coefficient c were found to produce better results than hierarchical algorithms unlike several other community detection methods the proposed method effectively partitioned the networks into different community structures and indicated the correct number of communities

brief identification of linear systems with hard input nonlinearities of known structure this paper studies identification of systems with input nonlinearities of known structure for input nonlinearities parameterized by one parameter a deterministic approach is proposed based on the idea of separable least squares the identification problem is shown to be equivalent to an one dimensional minimization problem the method is very effective for several common static and nonstatic input nonlinearities for a general input nonlinearity a correlation analysis based identification algorithm is presented which is shown to be convergent

bounding the parameters of linear systems with input backlash in this note we present a two stage procedure for deriving parameters bounds of linear systems with input backlash when the output measurement errors are bounded first using steady state input output data parameters of the nonlinear dynamic block are tightly bounded then given a suitable prbs input sequence we evaluate tight bounds on the unmeasurable inner signal which together with noisy output measurements are employed for bounding the parameters of the linear dynamic system

new monte carlo algorithm entropic sampling we present a new monte carlo sampling algorithm with which one can obtain any desired distribution of the sampling in one monte carlo simulation the free energy and the entropy of a system can thus be obtained from a simple exercise of this algorithm the main idea is to sample directly the entropy of a system at infinite temperature importance sampling is shown to be a particular case of the new algorithm the algorithm is tested against the exact partition function of the ital l 4 simple cubic ising model a comparison with the multicanonical ensemble for the ital l 12 ital q 10 potts model shows that the new algorithm is more general and more efficient

brief paper identification of hammerstein systems in presence of hysteresis backlash and hysteresis relay nonlinearities the problem of identifying hammerstein systems hamsys is addressed in presence of hysteresis backlash hb and hysteresis relay hr nonlinearities the memory feature makes it impossible to get a system parameterization involving linearly all unknown parameters therefore the linear subsystem and the nonlinear element are estimated separately the identification process involves appropriate system parameterizations and least squares like parameter estimators specific input signals are designed to guarantee persistent excitation pe and estimator consistency

optimization by multicanonical annealing and the traveling salesman problem we demonstrate a powerful and general simulated annealing method to study combinatorial optimization problems it combines the multicanonical method which samples directly the microcanonical entropy of the system with an elaborate but straightforward annealing scheme the idea is to fully utilize the information about the local entropy obtained during short monte carlo simulations for optimization in an iterative fashion we present results of an extensive investigation of the traveling salesman problem in a unit square

brief paper input output stability of systems with backlash the objective of this work is to study the stability of systems with backlash from an input output point of view in the first part an l analysis is addressed this analysis provides not only conditions for boundedness of the loop signals but also results on existence of solutions based on schauder fixed point theorem in the second part the system is studied using an l 2 or conic sector approach the backlash graph is confined into certain conic sector which is shown to be optimum or maximal the conic inequality induces frequencial conditions on the linear part which are further relaxed introducing dynamic multipliers in the third part both l and l 2 techniques are combined reaching a final criterion which results in a popov like stability condition

adjusting from disjoint to overlapping community detection of complex networks abstract the investigation of community structures is one of the most important problems in the field of complex networks and has countless applications in different disciplines biology computer social sciences etc many community detection algorithms have been developed in various fields recently the vast majority of these algorithms only find disjoint communities however in many real world networks communities often overlap to some extent in this paper we propose an efficient method for adjusting these classical algorithms to match the requirement for discovering overlapping communities in complex networks which is based on a local definition of community strength the method can in principle be applied with any clustering algorithm tests on a set of computer generated and real world networks give excellent results in particular we show that the method can also allow one to availably analyze the problem of unstable nodes in community detection which is very helpful for understanding the structural properties of the networks correctly and comprehensively

adaptive neural asymptotic control for uncertain nonlinear multiagent systems with a fuzzy dead zone constraint abstract a consensus control scheme is developed for a class of leader follower multiagent systems with a fuzzy dead zone constraint differently from previous work the method developed implements a predefined convergence of tracking errors for multiagent systems especially the dynamic model for each follower has a fuzzy dead zone input to solve the problem not only a set of smooth functions are used within the control design to increase the stability of the systems but also the unfuzziness and center of gravity method are used to analyze and process the actuator constraint model where each slope of the dead zone is uncertain and fuzzy it is verified that the method developed effectively guarantees that the tracking errors of multiagent systems can converge to a predefined interval that is the problem of better asymptotic consensus performance for nonlinear multiagent systems is solved simulation illustrates the results obtained

parameter identification of discontinuous hammerstein systems the paper deals with the parameter identification of hammerstein systems having discontinuous asymmetric nonlinearities two segment piecewise linear with preload and dead zone application of a simple decomposition technique provides a special form of hammerstein model with a minimum number of parameters the identification is carried out with only one record of measured input output data despite the dead zone to demonstrate the feasibility of the identification method some illustrative examples are included

adaptive fuzzy control for a class of nonlinear discrete time systems with backlash an adaptive fuzzy controller design is studied for uncertain nonlinear systems in this paper the considered systems are of the discrete time form in a triangular structure and include the backlash and the external disturbance by using the prediction function of future states the systems are transformed into an n step ahead predictor the fuzzy logic systems flss are used to approximate the unknown functions unknown backlash and backlash inversion respectively a discrete time tuning algorithm is developed to estimate the optimal fuzzy parameters compared with the previous works for the discrete time systems with backlash the main contributions of the paper are that 1 the rigorous restriction for the functional estimation error is removed and 2 the external disturbance is bounded but the bound is not required to be known a novel controller and the adaptation laws are constructed by using the discrete taylor series expansion and the difference lyapunov analysis and thus those limitations in the previous works are overcome it is proven that all the signals in the closed loop system are bounded and that the system output can be to follow the reference signal to a bounded compact set a simulation example is provided to illustrate the effectiveness of the proposed approach