Local multiresolution order in community detection

Community detection algorithms attempt to find the best clusters of nodes in an arbitrary complex network. Multi-scale ("multiresolution") community detection extends the problem to identify the best network scale(s) for these clusters. The latter task is generally accomplished by analyzing community stability simultaneously for all clusters in the network. In the current work, we extend this general approach to define local multiresolution methods, which enable the extraction of well-defined local communities even if the global community structure is vaguely defined in an average sense. Toward this end, we propose measures analogous to variation of information and normalized mutual information that are used to quantitatively identify the best resolution(s) at the community level based on correlations between clusters in independently-solved systems. We demonstrate our method on two constructed networks as well as a real network and draw inferences about local community strength. Our approach is independent of the applied community detection algorithm save for the inherent requirement that the method be able to identify communities across different network scales, with appropriate changes to account for how different resolutions are evaluated or defined in a particular community detection method. It should, in principle, easily adapt to alternative community comparison measures.Comment: 19 pages, 11 figure

Consensus clustering in complex networks

The community structure of complex networks reveals both their organization and hidden relationships among their constituents. Most community detection methods currently available are not deterministic, and their results typically depend on the specific random seeds, initial conditions and tie-break rules adopted for their execution. Consensus clustering is used in data analysis to generate stable results out of a set of partitions delivered by stochastic methods. Here we show that consensus clustering can be combined with any existing method in a self-consistent way, enhancing considerably both the stability and the accuracy of the resulting partitions. This framework is also particularly suitable to monitor the evolution of community structure in temporal networks. An application of consensus clustering to a large citation network of physics papers demonstrates its capability to keep track of the birth, death and diversification of topics.Comment: 11 pages, 12 figures. Published in Scientific Report

Fast Learning In Multilayered Neural Networks By Means Of Hybrid Evolutionary And Gradient Algorithms

: This paper describes two algorithms based on cooperative evolution of internal hidden network representations and a combination of global evolutionary and local search procedures. The obtained experimental results are better in comparison with prototype methods. It is demonstrated, that the applications of pure gradient or pure genetic algorithms to the network training problem is much worse than hybrid procedures, which reasonably combine the advantages of global as well as local search. 1. INTRODUCTION Artificial Neural Networks (ANN) allows to approach effectively a large class of applications including pattern recognition, visual perception, signal processing and control systems. The most progress in this field is related to invention of the error backpropagation algorithm by Rumelhart et al. [1]. Backpropagation is now a conventional procedure for ANN training. However, the backpropagation as well as its numerous modifications, often leads to typical problems for gradient descen..

Some New Features In Genetic Solution Of The Traveling Salesman Problem.

This paper describes some new features against premature convergence applied to genetic solution of the Traveling Salesman Problem (TSP). Authors practiced modified version of Greedy Crossover [1], which is less 'greedy' than a standard one, and operators brushing the population, which are helpful in getting out of local minima. As the result, the frequency of finding an optimal solution was improved with keeping good convergence. Developed application was tested on a number of benchmarks: Oliver's 30 [2], Eilon's 50 and Eilon's 75 [3] towns TSPs. In 30 towns problem the program seems to be always reaching an optimal solution. In 50 and 75 towns new better tours were found in comparison to all ones available to authors via papers on similar researches. Proposed approach in TSP solution is generalized as the social disasters technique. INTRODUCTION The TSP is defined as a task of finding of the shortest Hamiltonian cycle or path in complete graph of N nodes. It is a classic example of..

A Comparison of Bias Reduction Methods:Clustering versus Propensity Score Subclassification and Weighting

Propensity score (PS) adjustments have become popular methods used to improve estimates of treatment effects in quasi-experiments. Although researchers continue to develop PS methods, other procedures can also be effective in reducing selection bias. One of these uses clustering to create balanced groups. However, the success of this new method depends on its efficacy compared to that of the existing methods. Therefore, this comparative study used experimental and nonexperimental data to examine bias reduction, case retention, and covariate balance in the clustering method, PS subclassification, and PS weighting. In general, results suggest that the cluster-based methods reduced at least as much bias as the PS methods. Under certain conditions, the PS methods reduced more bias than the cluster-based method, and under other conditions the cluster-based methods were more advantageous. Although all methods were equally effective in retaining cases and balancing covariates, other data-specific conditions may likely favor the use of a cluster-based approach