Network theory

About: Network theory is a research topic. Over the lifetime, 2257 publications have been published within this topic receiving 109864 citations.

A Quick& Practicable Power Flow Tracing Method On

Abstract: In a competitive market enwronment, "fairly allocate the total cost of transmission" which is identical with the actual utilization of the network is most important. Based on network theory and graph theorem, this paper theoretically proves and extends the proportional sharing principle, With it, the employ- ment of transmission lines by different generators and loads can be confirmed theoretically, while energy consumers can be )m- partlally, rationally charged, and fleetly and truly ascertained according to the actual utlllzatlon of the network. Also based on this principle, many problelms In electricity energy market field can be settled.

A comparison of procedures for structural learning of biological networks

Abstract: Over the past years, microarray technologies have produced a tremendous amount of gene expression data. The availability of these data has motivated researchers to assess genes function and to gain a deeper understanding of the cellular processes, using network theory as tool for the analysis. An elegant framework for modeling and inferring network structures in biological systems is provided by graphical models. They allow the stochastic description of network associations and dependence structures in complex highly structured data. However, typically gene expression data set includes a large number of variables but only few samples making standard graphical model theories inapplicable. The issues presented by genetic data have led to further extend the theory of graphical models to allow their applications in this area. The main aim of this thesis is the comparison of recent procedures, which estimate sparse concentration matrices and learn the structure of biological networks, through the use of both simulated and real data. The compared procedures are: G-Lasso algorithm (Friedman et al., 2008), Shrinkage estimator with empirical Bayes approach for model selection (Schafer and Strimmer, 2005a, 2005b), PC-algorithm (Kalisch and Buhlmann, 2007). When n > p, we consider also the simple frequentist approach based on MLE and t-test for model selection (see Lauritzen, 1996). Regarding the simulated data, for having a realistic simulation of the biological structures, the data have the peculiarity to reproduce few gene regulatory network structures of interest and they are generated by exploiting some properties of the Cholesky decomposition of a matrix. Concerning the real data, we consider the analysis of one of the best characterized system: Escherichia coli. A large part of its transcriptional regulatory network is known, hence it can be used as a gold-standard to assess the performance of different procedures in the comparative study.

Parallelization of game theoretic centrality algorithms

Abstract: Communication has become a lot easier with the advent of easy and cheap means of reaching people across the globe. This has allowed the development of large networked communities and, with the technology available to track them, has opened up the study of social networks at unprecedented scales. This has necessitated the scaling up of various network analysis algorithms that have been proposed earlier in the literature. While some algorithms can be readily adapted to large networks, in many cases the adaptation is not trivial. In this work, we explore the scaling up of a class of node centrality algorithms based on cooperative game theory. These were proposed earlier as an efficient alternatives to traditional measure of information diffusion centrality. We present here distributed versions of these algorithms in a Map-Reduce framework, currently the most popular distributed computing paradigm. We empirically demonstrate the scaling behavior of our algorithm on very large synthetic networks thereby establishing the utility of these methods in settings such as online social networks.

Differences Between Jurors in Classical Music Competitions: The MCDM and Network Theory Approaches

Abstract: This paper analyses the voting in two of the major international classical music competitions, which were held recently, viz. the International Henryk Wieniawski Violin Competition and the International Chopin Piano Competition, as well as the hypothesis, raised in some media reports, that there were juror cliques in the Wieniawski Competition. Network theory is used to compare the rankings of the two Chopin competitions. Jurors are nodes and they are linked if the correlation between the ordered list of competitors, as measured by the Kendall rank correlation coefficient, exceeds a given threshold value. The obtained networks were found linked in the case of the Chopin Competition, but disconnected in the case of the Wieniawski Competition. The results indicate that there may have been cliques in the Wieniawski Competition, but not in the Chopin Competition. The problem can be descibed in MCDM terminology by labelling the contestants ’variants’ and the jurors (or, more precisely, their musical preferences) – ’criteria’. The similarity of any two criteria is measured by correlating the orders of the alternatives (i.e. variants) that * SGH Warsaw School of Economics, Department of Mathematics and Mathematical Economics, Warsaw, Poland, e-mail: honorata@sgh.waw.pl, ORCID: 0000-0001-8249-2368. ** SGHWarsaw School of Economics, Department of Mathematics and Mathematical Economics, Warsaw, Poland, e-mail: pzawis@sgh.waw.pl, ORCID: 0000-0002-5297-7644. 94 H. Sosnowska, P. Zawiślak result from applying them. The problem of juror cliques is thereby transformed into one of finding groups of criteria that are similar in the case of these variants.

Shapeshifting to address complexity: Advancing a typology of network evolution and transformation

Abstract: In practice, health and social services are delivered through purpose-oriented networks (PONs) that are often favored by government and philanthropic investment as an effective means for collectively solving complex social problems. Current theories examine the evolution of these groups by resting on the traditional organizational forms of market, hierarchy, and network, without a consideration of trajectories that show movement between organizational forms over time. This article utilizes the network itself as the unit of analysis within a larger network domain to examine the most common trajectories and changes in organizational forms over time. To date, little theory has been developed or applied to account for both endogenous characteristics and exogenous system-wide dynamics and their longitudinal effects on networks. As is appropriate in the early stages of developing new theories, this article addresses the foundational steps of first clarifying the phenomenon of interest with the creation of a typology of the ways in which networks evolve as organizational forms and suggesting future network-level and network domain research agendas.