Academic literature on the topic 'Statistical graphs'

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Journal articles on the topic "Statistical graphs"

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Haigh, William E. "Statistical Graphs and Logo." School Science and Mathematics 89, no. 3 (March 1989): 228–38. http://dx.doi.org/10.1111/j.1949-8594.1989.tb11914.x.

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Nowicki, Krzysztof. "Asymptotic Poisson distributions with applications to statistical analysis of graphs." Advances in Applied Probability 20, no. 02 (June 1988): 315–30. http://dx.doi.org/10.1017/s0001867800016992.

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Various types of graph statistics for graphs and digraphs are presented as numerators of incompleteU-statistics, with symmetric and asymmetric kernels, respectively. Thus, asymptotic Poisson limits of these statistics are provided by using limit theorems for the sums of dissociated random variables. Several applications to statistical analysis of graphs are given.
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Nowicki, Krzysztof. "Asymptotic Poisson distributions with applications to statistical analysis of graphs." Advances in Applied Probability 20, no. 2 (June 1988): 315–30. http://dx.doi.org/10.2307/1427392.

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Various types of graph statistics for graphs and digraphs are presented as numerators of incomplete U-statistics, with symmetric and asymmetric kernels, respectively. Thus, asymptotic Poisson limits of these statistics are provided by using limit theorems for the sums of dissociated random variables. Several applications to statistical analysis of graphs are given.
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Ghafouri, Saeid, and Seyed Hossein Khasteh. "A survey on exponential random graph models: an application perspective." PeerJ Computer Science 6 (April 6, 2020): e269. http://dx.doi.org/10.7717/peerj-cs.269.

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The uncertainty underlying real-world phenomena has attracted attention toward statistical analysis approaches. In this regard, many problems can be modeled as networks. Thus, the statistical analysis of networked problems has received special attention from many researchers in recent years. Exponential Random Graph Models, known as ERGMs, are one of the popular statistical methods for analyzing the graphs of networked data. ERGM is a generative statistical network model whose ultimate goal is to present a subset of networks with particular characteristics as a statistical distribution. In the context of ERGMs, these graph’s characteristics are called statistics or configurations. Most of the time they are the number of repeated subgraphs across the graphs. Some examples include the number of triangles or the number of cycle of an arbitrary length. Also, any other census of the graph, as with the edge density, can be considered as one of the graph’s statistics. In this review paper, after explaining the building blocks and classic methods of ERGMs, we have reviewed their newly presented approaches and research papers. Further, we have conducted a comprehensive study on the applications of ERGMs in many research areas which to the best of our knowledge has not been done before. This review paper can be used as an introduction for scientists from various disciplines whose aim is to use ERGMs in some networked data in their field of expertise.
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Burda, Zdzisław, Jerzy Jurkiewicz, and André Krzywicki. "Statistical mechanics of random graphs." Physica A: Statistical Mechanics and its Applications 344, no. 1-2 (December 2004): 56–61. http://dx.doi.org/10.1016/j.physa.2004.06.087.

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LEWANDOWSKY, STEPHAN, and IAN SPENCE. "The Perception of Statistical Graphs." Sociological Methods & Research 18, no. 2-3 (November 1989): 200–242. http://dx.doi.org/10.1177/0049124189018002002.

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Hosamani, S. M., V. B. Awati, and R. M. Honmore. "On graphs with equal dominating and c-dominating energy." Applied Mathematics and Nonlinear Sciences 4, no. 2 (December 24, 2019): 503–12. http://dx.doi.org/10.2478/amns.2019.2.00047.

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AbstractGraph energy and domination in graphs are most studied areas of graph theory. In this paper we try to connect these two areas of graph theory by introducing c-dominating energy of a graph G. First, we show the chemical applications of c-dominating energy with the help of well known statistical tools. Next, we obtain mathematical properties of c-dominating energy. Finally, we characterize trees, unicyclic graphs, cubic and block graphs with equal dominating and c-dominating energy.
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Livingston, Mark A., Laura Matzen, Derek Brock, Andre Harrison, and Jonathan W. Decker. "Testing the Value of Salience in Statistical Graphs." Electronic Imaging 2021, no. 1 (January 18, 2021): 329–1. http://dx.doi.org/10.2352/issn.2470-1173.2021.1.vda-329.

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Expert advice and conventional wisdom say that important information within a statistical graph should be more salient than the other components. If readers are able to find relevant information quickly, in theory, they should perform better on corresponding response tasks. To our knowledge, this premise has not been thoroughly tested. We designed two types of salient cues to draw attention to task-relevant information within statistical graphs. One type primarily relied on text labels and the other on color highlights. The utility of these manipulations was assessed with groups of questions that varied from easy to hard. We found main effects from the use of our salient cues. Error and response time were reduced, and the portion of eye fixations near the key information increased. An interaction between the cues and the difficulty of the questions was also observed. In addition, participants were given a baseline skills test, and we report the corresponding effects. We discuss our experimental design, our results, and implications for future work with salience in statistical graphs.
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Salti, Dror, and Yakir Berchenko. "Random Intersection Graphs and Missing Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 5579–85. http://dx.doi.org/10.1609/aaai.v34i04.6010.

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Random-graphs and statistical inference with missing data are two separate topics that have been widely explored each in its field. In this paper we demonstrate the relationship between these two different topics and take a novel view of the data matrix as a random intersection graph. We use graph properties and theoretical results from random-graph theory, such as connectivity and the emergence of the giant component, to identify two threshold phenomena in statistical inference with missing data: loss of identifiability and slower convergence of algorithms that are pertinent to statistical inference such as expectation-maximization (EM). We provide two examples corresponding to these threshold phenomena and illustrate the theoretical predictions with simulations that are consistent with our reduction.
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Sudev, N. K., K. P. Chithra, K. A. Germina, S. Satheesh, and Johan Kok. "On certain coloring parameters of Mycielski graphs of some graphs." Discrete Mathematics, Algorithms and Applications 10, no. 03 (June 2018): 1850030. http://dx.doi.org/10.1142/s1793830918500301.

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Coloring the vertices of a graph [Formula: see text] according to certain conditions can be considered as a random experiment and a discrete random variable [Formula: see text] can be defined as the number of vertices having a particular color in the proper coloring of [Formula: see text]. The concepts of mean and variance, two important statistical measures, have also been introduced to the theory of graph coloring and determined the values of these parameters for a number of standard graphs. In this paper, we discuss the coloring parameters of the Mycielskian of certain standard graphs.
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Dissertations / Theses on the topic "Statistical graphs"

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Ruan, Da. "Statistical methods for comparing labelled graphs." Thesis, Imperial College London, 2014. http://hdl.handle.net/10044/1/24963.

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Due to the availability of the vast amount of graph-structured data generated in various experiment settings (e.g., biological processes, social connections), the need to rapidly identify network structural differences is becoming increasingly prevalent. In many fields, such as bioinformatics, social network analysis and neuroscience, graphs estimated from the same experimental settings are always defined on a fixed set of objects. We formalize such a problem as a labelled graph comparison problem. The main issue in this area, i.e. measuring the distance between graphs, has been extensively studied over the past few decades. Although a large distance value constitutes evidence of difference between graphs, we are more interested in the issue of inferentially justifying whether a distance value as large or larger than the observed distance could have been obtained simply by chance. However, little work has been done to provide the procedures of statistical inference necessary to formally answer this question. Permutation-based inference has been proposed as a theoretically sound approach and a natural way of tackling such a problem. However, the common permutation procedure is computationally expensive, especially for large graphs. This thesis contributes to the labelled graph comparison problem by addressing three different topics. Firstly, we analyse two labelled graphs by inferentially justifying their independence. A permutation-based testing procedure based on Generalized Hamming Distance (GHD) is proposed. We show rigorously that the permutation distribution is approximately normal for a large network, under three graph models with two different types of edge weights. The statistical significance can be evaluated without the need to resort to computationally expensive permutation procedures. Numerical results suggest the validity of this approximation. With the Topological Overlap edge weight, we suggest that the GHD test is a more powerful test to identify network differences. Secondly, we tackle the problem of comparing two large complex networks in which only localized topological differences are assumed. By applying the normal approximation for the GHD test, we propose an algorithm that can effectively detect localised changes in the network structure from two large complex networks. This algorithm is quickly and easily implemented. Simulations and applications suggest that it is a useful tool to detect subtle differences in complex network structures. Finally, we address the problem of comparing multiple graphs. For this topic, we analyse two different problems that can be interpreted as corresponding to two distinct null hypotheses: (i) a set of graphs are mutually independent; (ii) graphs in one set are independent of graphs in another set. Applications for the multiple graphs problem are commonly found in social network analysis (i) or neuroscience (ii). However, little work has been done to inferentially address the problem of comparing multiple networks. We propose two different statistical testing procedures for (i) and (ii), by again using a normality approximation for GHD. We extend the normality of GHD for the two graphs case to multiple cases, for hypotheses (i) and (ii), with two different permutation strategies. We further build a link between the test of group independence to an existing method, namely the Multivariate Exponential Random Graph Permutation model (MERGP). We show that by applying asymptotic normality, the maximum likelihood estimate of MERGP can be analytically derived. Therefore, the original, computationally expensive, inferential procedure of MERGP can be abandoned.
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Cerqueira, Andressa. "Statistical inference on random graphs and networks." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/45/45133/tde-04042018-094802/.

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In this thesis we study two probabilistic models defined on graphs: the Stochastic Block model and the Exponential Random Graph. Therefore, this thesis is divided in two parts. In the first part, we introduce the Krichevsky-Trofimov estimator for the number of communities in the Stochastic Block Model and prove its eventual almost sure convergence to the underlying number of communities, without assuming a known upper bound on that quantity. In the second part of this thesis we address the perfect simulation problem for the Exponential random graph model. We propose an algorithm based on the Coupling From The Past algorithm using a Glauber dynamics. This algorithm is efficient in the case of monotone models. We prove that this is the case for a subset of the parametric space. We also propose an algorithm based on the Backward and Forward algorithm that can be applied for monotone and non monotone models. We prove the existence of an upper bound for the expected running time of both algorithms.
Nessa tese estudamos dois modelos probabilísticos definidos em grafos: o modelo estocástico por blocos e o modelo de grafos exponenciais. Dessa forma, essa tese está dividida em duas partes. Na primeira parte nós propomos um estimador penalizado baseado na mistura de Krichevsky-Trofimov para o número de comunidades do modelo estocástico por blocos e provamos sua convergência quase certa sem considerar um limitante conhecido para o número de comunidades. Na segunda parte dessa tese nós abordamos o problema de simulação perfeita para o modelo de grafos aleatórios Exponenciais. Nós propomos um algoritmo de simulação perfeita baseado no algoritmo Coupling From the Past usando a dinâmica de Glauber. Esse algoritmo é eficiente apenas no caso em que o modelo é monotóno e nós provamos que esse é o caso para um subconjunto do espaço paramétrico. Nós também propomos um algoritmo de simulação perfeita baseado no algoritmo Backward and Forward que pode ser aplicado à modelos monótonos e não monótonos. Nós provamos a existência de um limitante superior para o número esperado de passos de ambos os algoritmos.
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Vohra, Neeru Rani. "Three dimensional statistical graphs, visual cues and clustering." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp03/MQ56213.pdf.

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Chandrasekaran, Venkat. "Convex optimization methods for graphs and statistical modeling." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66002.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 209-220).
An outstanding challenge in many problems throughout science and engineering is to succinctly characterize the relationships among a large number of interacting entities. Models based on graphs form one major thrust in this thesis, as graphs often provide a concise representation of the interactions among a large set of variables. A second major emphasis of this thesis are classes of structured models that satisfy certain algebraic constraints. The common theme underlying these approaches is the development of computational methods based on convex optimization, which are in turn useful in a broad array of problems in signal processing and machine learning. The specific contributions are as follows: -- We propose a convex optimization method for decomposing the sum of a sparse matrix and a low-rank matrix into the individual components. Based on new rank-sparsity uncertainty principles, we give conditions under which the convex program exactly recovers the underlying components. -- Building on the previous point, we describe a convex optimization approach to latent variable Gaussian graphical model selection. We provide theoretical guarantees of the statistical consistency of this convex program in the high-dimensional scaling regime in which the number of latent/observed variables grows with the number of samples of the observed variables. The algebraic varieties of sparse and low-rank matrices play a prominent role in this analysis. -- We present a general convex optimization formulation for linear inverse problems, in which we have limited measurements in the form of linear functionals of a signal or model of interest. When these underlying models have algebraic structure, the resulting convex programs can be solved exactly or approximately via semidefinite programming. We provide sharp estimates (based on computing certain Gaussian statistics related to the underlying model geometry) of the number of generic linear measurements required for exact and robust recovery in a variety of settings. -- We present convex graph invariants, which are invariants of a graph that are convex functions of the underlying adjacency matrix. Graph invariants characterize structural properties of a graph that do not depend on the labeling of the nodes; convex graph invariants constitute an important subclass, and they provide a systematic and unified computational framework based on convex optimization for solving a number of interesting graph problems. We emphasize a unified view of the underlying convex geometry common to these different frameworks. We describe applications of both these methods to problems in financial modeling and network analysis, and conclude with a discussion of directions for future research.
by Venkat Chandrasekaran.
Ph.D.
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Piotet, Fabien. "Statistical properties of the eigenfunctions on quantum graphs." Thesis, University of Bristol, 2009. http://hdl.handle.net/1983/af3ec57a-5d16-4995-936f-c310696f1093.

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Phadnis, Miti. "Statistical Analysis of Linear Analog Circuits Using Gaussian Message Passing in Factor Graphs." DigitalCommons@USU, 2009. https://digitalcommons.usu.edu/etd/504.

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This thesis introduces a novel application of factor graphs to the domain of analog circuits. It proposes a technique of leveraging factor graphs for performing statistical yield analysis of analog circuits that is much faster than the standard Monte Carlo/Simulation Program With Integrated Circuit Emphasis (SPICE) simulation techniques. We have designed a tool chain to model an analog circuit and its corresponding factor graph and then use a Gaussian message passing approach along the edges of the graph for yield calculation. The tool is also capable of estimating unknown parameters of the circuit given known output statistics through backward message propagation in the factor graph. The tool builds upon the concept of domain-specific modeling leveraged for modeling and interpreting different kinds of analog circuits. Generic Modeling Environment (GME) is used to design modeling environment for analog circuits. It is a configurable tool set that supports creation of domain-specific design environments for different applications. This research has developed a generalized methodology that could be applied towards design automation of different kinds of analog circuits, both linear and nonlinear. The tool has been successfully used to model linear amplifier circuits and a nonlinear Metal Oxide Semiconductor Field Effect Transistor (MOSFET) circuit. The results obtained by Monte Carlo simulations performed on these circuits are used as a reference in the project to compare against the tool's results. The tool is tested for its efficiency in terms of time and accuracy against the standard results.
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Hamdi, Maziyar. "Statistical signal processing on dynamic graphs with applications in social networks." Thesis, University of British Columbia, 2015. http://hdl.handle.net/2429/56256.

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Due to the proliferation of social networks and their significant effects on our day-to-day activities, there has been a growing interest in modeling and analyzing behavior of agents in social networks over the past decade. The unifying theme of this thesis is to develop a set of mathematical theories and algorithmic tools for different estimation and sensing problems over graphs with applications to social networks. The first part of this dissertation is devoted to multi-agent Bayesian estimation and learning problem in social networks. We consider a set of agents that interact over a network to estimate an unknown parameter called state of nature. As a result of the recursive nature of Bayesian models and the correlation introduced by the structure of the underlying communication graph, information collected by one agent can be mistakenly considered independent, that is, mis-information prop- agation, also known as data incest arises. This part presents data incest removal algorithms that ensure complete mitigation of the mis-information associated with the estimates of agents in two different information exchange patterns: First, a scenario where beliefs (posterior distribution of state of nature) are transmitted over the network. Second, a social learning context where agents map their local beliefs into a finite set of actions and broadcast their actions to other agents. We also present a necessary and sufficient condition on the structure of information flow graph to mitigate mis-information propagation. The second part of the thesis considers a Markov-modulated duplication-deletion random graph where at each time instant, one node can either join or leave the network; the probabilities of joining or leaving evolve according to the realization of a finite state Markov chain. This part presents two results. First, motivated by social network applications, the asymptotic behavior of the degree distribution is analyzed. Second, a stochastic approximation algorithm is presented to track empirical degree distribution as it evolves over time. The tracking performance of the algorithm is analyzed in terms of mean square error and a functional central limit theorem is presented for the asymptotic tracking error.
Applied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
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Alberici, Diego. "Statistical Mechanics of Monomer-Dimer Models on Complete and Erdös-Rényi Graphs." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2012. http://amslaurea.unibo.it/4169/.

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Nella tesi sono trattate due famiglie di modelli meccanico statistici su vari grafi: i modelli di spin ferromagnetici (o di Ising) e i modelli di monomero-dimero. Il primo capitolo è dedicato principalmente allo studio del lavoro di Dembo e Montanari, in cui viene risolto il modello di Ising su grafi aleatori. Nel secondo capitolo vengono studiati i modelli di monomero-dimero, a partire dal lavoro di Heilemann e Lieb,con l'intento di dare contributi nuovi alla teoria. I principali temi trattati sono disuguaglianze di correlazione, soluzioni esatte su alcuni grafi ad albero e sul grafo completo, la concentrazione dell'energia libera intorno al proprio valor medio sul grafo aleatorio diluito di Erdös-Rényi.
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Díaz-Levicoy, Danilo, Miluska Osorio, Pedro Arteaga, and Francisco Rodríguez-Alveal. "Gráficos estadísticos en libros de texto de matemática de educación primaria en Perú." BOLEMA Departamento de Matematica, 2018. http://hdl.handle.net/10757/624628.

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El presente artículo presenta los resultados del análisis de los gráficos estadísticos según las directrices curriculares y su implementación en dieciocho libros de texto de matemática de Educación Primaria en Perú, los que corresponden a tres series completas y de diferentes editoriales. En ellos se analizan, mediante análisis de contenido, las secciones en las que aparecen estas representaciones, identificado el tipo de actividad que se plantea, los gráficos involucrados, el nivel de lectura y el nivel de complejidad semiótica involucrado. Los libros de texto se adecúan parcialmente a las directrices curriculares en cuanto a la presentación de los gráficos por nivel educativo, el número de actividades propuestas por las tres editoriales es similar. La principal actividad que se solicita en los libros es de calcular y construir. Se observa un predominio del gráfico de barras, un nivel de lectura básico y la representación de una distribución de datos en el gráfico.
Revisión por pares
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Leger, Jean-Benoist. "Modelling the topology of ecological bipartite networks with statistical models for heterogeneous random graphs." Paris 7, 2014. http://www.theses.fr/2014PA077185.

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Un réseau écologique constitue une représentation de l'ensemble des interactions entre espèces dans un contexte donné. L'analyse de la structure topologique de ces réseaux permet aux écologues d'identifier et de comprendre les processus sous-jacents. La détection de sous-groupes d'espèces interagissant fortement ensemble, souvent nommés communautés o compartiments, est un des principaux moyens pour interpréter la structure sous-jacente des réseaux. Il existe de nombreuses méthodes de classification non supervisée, qui peuvent être utilisées pour analyser des réseaux écologiques. L'analyse des réseaux est actuellement un sujet de recherche en pleine expansion avec des applications dans des domaines de recherches variés. À notre connaissance, il n'existe pas de comparaison des méthodes de classification non supervisée dans le cas des réseaux écologique. Nous avons effectué une revue des méthodes disponibles de classification non supervisées des noeuds, et nous les avons comparé dans un contexte écologique. Afin d'évaluer la contribution des différents processus expliquant la structure d'un réseau, nous avons introduit de l'information extérieure au réseau. Nous avons analysé deux réseaux d'interaction arbre-champignon et arbre-insecte. Ces résultats sont préliminaires, mais la méthode semble ouvrir des perspectives intéressantes en écologie. Nous avons également étudié un réseau écologique de nature différente, un réseau de reproduction entre arbres. Nous avons utilisé ces résultats pour discuter d'un concept central en écologie, le concept d'espèce
An ecological network is a representation of the whole set of interactions between species in a given context. Ecological scientists analyse the topological structure of such networks, in order to understand the underlying processes. The identification of sub-groups of highly-interacting species (usually called communities, or compartments) is an important stream of research. The most popular method for the search of communities in ecological networks is the modularity optimization method. However this popularity is more due to the first paper published on this topic than to a rational choice based on solid grounds. There are many other clustering methods that could be used to delimit communities in ecological networks. The analysis of complex networks is indeed a rapidly growing topic with many applications in several scientific fields. To our knowledge, no comparison of different clustering methods is available in the case of ecological networks. Here we reviewed the whole set of methods available for clustering networks and we compared them using an ecological benchmark. In order to assess the relative contribution of several processes to the network structure, we integrated exogenous information in the clustering model. We analysed two bipartite antagonistic networks with this method, a tree-fungus and tree-insect network. The results are still preliminary but the method seems to us very promising for future ecological studies. Finally we searched communities in a different kind of network, a mating network between individuals belonging to two hybridizing tree species. We used our results to discuss a concept which is central in ecology, the species concept
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Books on the topic "Statistical graphs"

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Random graphs for statistical pattern recognition. Hoboken, NJ: Wiley & Sons, 2002.

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Marchette, David J. Random Graphs for Statistical Pattern Recognition. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2004. http://dx.doi.org/10.1002/047172209x.

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Jaroslav, Nešetřil, and Winkler P. 1946-, eds. Graphs, morphisms, and statistical physics: DIMACS Workshop Graphs, Morphisms and Statistical Physics, March 19-21, 2001, DIMACS Center. Providence, RI: American Mathematical Society, 2004.

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Aldrich, James O. Building SPSS graphs to understand data. Thousand Oaks: SAGE Publications, 2013.

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Aldrich, James O. Building SPSS graphs to understand data. Thousand Oaks: SAGE Publications, 2013.

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Bayesian networks and decision graphs. New York: Springer, 2001.

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W, Dyal William, Eddins Donald L, and Centers for Disease Control (U.S.), eds. Descriptive statistics: Tables, graphs, & charts. Atlanta, Ga: U.S. Dept. of Health and Human Services, Public Health Service, Centers for Disease Control, 1988.

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Vladas, Sidoravicius, and Smirnov S. (Stanislav) 1970-, eds. Probability and statistical physics in St. Petersburg: St. Petersburg School in Probability and Statistical Physics : June 18-29, 2012 : St. Petersburg State University, St. Petersburg, Russia. Providence, Rhode Island: American Mathematical Society, 2015.

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Institute, SAS, ed. SAS system for statistical graphics. Cary, NC: SAS Institute, 1991.

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Creating more effective graphs. Hoboken, N.J: Wiley-Interscience, 2005.

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Book chapters on the topic "Statistical graphs"

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Heiberger, Richard M., and Burt Holland. "Graphs." In Statistical Analysis and Data Display, 85–121. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2122-5_4.

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Heiberger, Richard M., and Burt Holland. "Graphs." In Statistical Analysis and Data Display, 63–89. New York, NY: Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4284-8_4.

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Hanna, Akram R. G., Christopher Rao, and Thanos Athanasiou. "Graphs in Statistical Analysis." In Key Topics in Surgical Research and Methodology, 441–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-71915-1_35.

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Kolaczyk, Eric D., and Gábor Csárdi. "Statistical Models for Network Graphs." In Use R!, 85–109. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0983-4_6.

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Boutillier, Cédric, and Béatrice de Tilière. "Statistical Mechanics on Isoradial Graphs." In Probability in Complex Physical Systems, 491–512. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23811-6_20.

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Kolaczyk, Eric D., and Gábor Csárdi. "Statistical Models for Network Graphs." In Use R!, 87–113. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44129-6_6.

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Loebl, Martin. "Geometric representations of graphs." In Discrete Mathematics in Statistical Physics, 77–100. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9329-1_5.

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Karwa, Vishesh, Aleksandra B. Slavković, and Pavel Krivitsky. "Differentially Private Exponential Random Graphs." In Privacy in Statistical Databases, 143–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11257-2_12.

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Andersson, Håkan, and Tom Britton. "Epidemics and graphs." In Stochastic Epidemic Models and Their Statistical Analysis, 63–72. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-1-4612-1158-7_7.

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Mansmann, Ulrich, Markus Schmidberger, Ralf Strobl, and Vindi Jurinovic. "Indirect Comparison of Interaction Graphs." In Statistical Modelling and Regression Structures, 249–65. Heidelberg: Physica-Verlag HD, 2009. http://dx.doi.org/10.1007/978-3-7908-2413-1_14.

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Conference papers on the topic "Statistical graphs"

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Karaaslanli, Abdullah, and Selin Aviyente. "Graph Learning From Noisy and Incomplete Signals on Graphs." In 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021. http://dx.doi.org/10.1109/ssp49050.2021.9513838.

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Bolanos, Marcos E., Selin Aviyente, and Hayder Radha. "Graph entropy rate minimization and the compressibility of undirected binary graphs." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319634.

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Ferres, Leo, Petro Verkhogliad, Gitte Lindgaard, Louis Boucher, Antoine Chretien, and Martin Lachance. "Improving accessibility to statistical graphs." In the 9th international ACM SIGACCESS conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1296843.1296857.

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Arteaga Cezón, Pedro, Jose Manuel Vigo, and Carmen Batanero. "READING LEVELS OF STATISTICAL GRAPHS." In 10th annual International Conference of Education, Research and Innovation. IATED, 2017. http://dx.doi.org/10.21125/iceri.2017.0719.

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Aghagolzadeh, Mohammad, Iman Barjasteh, and Hayder Radha. "Transitivity matrix of social network graphs." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319644.

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Hafidi, Hakim, Mounir Ghogho, Philippe Ciblat, and Ananthram Swami. "Bayesian Node Classification for Noisy Graphs." In 2021 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021. http://dx.doi.org/10.1109/ssp49050.2021.9513801.

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Zens, Richard, and Hermann Ney. "Word graphs for statistical machine translation." In the ACL Workshop. Morristown, NJ, USA: Association for Computational Linguistics, 2005. http://dx.doi.org/10.3115/1654449.1654491.

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Rao, Vasant, Debjit Sinha, Nitin Srimal, and Prabhat K. Maurya. "Statistical path tracing in timing graphs." In DAC '16: The 53rd Annual Design Automation Conference 2016. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2897937.2898096.

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Tsitsvero, Mikhail, Pierre Borgnat, and Paulo Goncalves. "Multidimensional Analytic Signal with Application on Graphs." In 2018 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2018. http://dx.doi.org/10.1109/ssp.2018.8450803.

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Romero, Daniel, Meng Ma, and Georgios B. Giannakis. "Estimating signals over graphs via multi-kernel learning." In 2016 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2016. http://dx.doi.org/10.1109/ssp.2016.7551714.

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Reports on the topic "Statistical graphs"

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Miller, Willard, and Jr. Robustness, Diagnostics, Computing and Graphics in Statistics. Fort Belvoir, VA: Defense Technical Information Center, January 1990. http://dx.doi.org/10.21236/ada222888.

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W. Davis and D. Mastrovito. DbAccess: Interactive Statistics and Graphics for Plasma Physics Databases. Office of Scientific and Technical Information (OSTI), October 2003. http://dx.doi.org/10.2172/820083.

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Newton, H. J. Computing Science and Statistics. Volume 24. Graphics and Visualization. Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada265181.

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Kennedy, C. R. Statistical characterization of three grades of large billet-graphites: Stackpole 2020, Union Carbide TS1792, and Toyo Tanso IG11. Office of Scientific and Technical Information (OSTI), September 1987. http://dx.doi.org/10.2172/770926.

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Schulz, Jan, Daniel Mayerhoffer, and Anna Gebhard. A Network-Based Explanation of Perceived Inequality. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49393.

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Across income groups and countries, the public perception of economic inequality and many other macroeconomic variables such as inflation or unemployment rates is spectacularly wrong. These misperceptions have far-reaching consequences, as it is perceived inequality, not actual inequality informing redistributive preferences. The prevalence of this phenomenon is independent of social class and welfare regime, which suggests the existence of a common mechanism behind public perceptions. We propose a network-based explanation of perceived inequality building on recent advances in random geometric graph theory. The literature has identified several stylised facts on how individual perceptions respond to actual inequality and how these biases vary systematically along the income distribution. Our generating mechanism can replicate all of them simultaneously. It also produces social networks that exhibit salient features of real-world networks; namely, they cannot be statistically distinguished from small-world networks, testifying to the robustness of our approach. Our results, therefore, suggest that homophilic segregation is a promising candidate to explain inequality perceptions with strong implications for theories of consumption behaviour.
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Brown, Yolanda, Twonia Goyer, and Maragaret Harvey. Heart Failure 30-Day Readmission Frequency, Rates, and HF Classification. University of Tennessee Health Science Center, December 2020. http://dx.doi.org/10.21007/con.dnp.2020.0002.

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30 Day Hospital Readmission Rates, Frequencies, and Heart Failure Classification for Patients with Heart Failure Background Congestive heart failure (CHF) is the leading cause of mortality, morbidity, and disability worldwide among patients. Both the incidence and the prevalence of heart failure are age dependent and are relatively common in individuals 40 years of age and older. CHF is one of the leading causes of inpatient hospitalization readmission in the United States, with readmission rates remaining above the 20% goal within 30 days. The Center for Medicare and Medicaid Services imposes a 3% reimbursement penalty for excessive readmissions including those who are readmitted within 30 days from prior hospitalization for heart failure. Hospitals risk losing millions of dollars due to poor performance. A reduction in CHF readmission rates not only improves healthcare system expenditures, but also patients’ mortality, morbidity, and quality of life. Purpose The purpose of this DNP project is to determine the 30-day hospital readmission rates, frequencies, and heart failure classification for patients with heart failure. Specific aims include comparing computed annual re-admission rates with national average, determine the number of multiple 30-day re-admissions, provide descriptive data for demographic variables, and correlate age and heart failure classification with the number of multiple re-admissions. Methods A retrospective chart review was used to collect hospital admission and study data. The setting occurred in an urban hospital in Memphis, TN. The study was reviewed by the UTHSC Internal Review Board and deemed exempt. The electronic medical records were queried from July 1, 2019 through December 31, 2019 for heart failure ICD-10 codes beginning with the prefix 150 and a report was generated. Data was cleaned such that each patient admitted had only one heart failure ICD-10 code. The total number of heart failure admissions was computed and compared to national average. Using age ranges 40-80, the number of patients re-admitted withing 30 days was computed and descriptive and inferential statistics were computed using Microsoft Excel and R. Results A total of 3524 patients were admitted for heart failure within the six-month time frame. Of those, 297 were re-admitted within 30 days for heart failure exacerbation (8.39%). An annual estimate was computed (16.86%), well below the national average (21%). Of those re-admitted within 30 days, 50 were re-admitted on multiple occasions sequentially, ranging from 2-8 re-admissions. The median age was 60 and 60% male. Due to the skewed distribution (most re-admitted twice), nonparametric statistics were used for correlation. While graphic display of charts suggested a trend for most multiple re-admissions due to diastolic dysfunction and least number due to systolic heart failure, there was no statistically significant correlation between age and number or multiple re-admissions (Spearman rank, p = 0.6208) or number of multiple re-admissions and heart failure classification (Kruskal Wallis, p =0.2553).
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Lavadenz, Magaly, Sheila Cassidy, Elvira G. Armas, Rachel Salivar, Grecya V. Lopez, and Amanda A. Ross. Sobrato Early Academic Language (SEAL) Model: Final Report of Findings from a Four-Year Study. Center for Equity for English Learners, Loyola Marymount University, 2020. http://dx.doi.org/10.15365/ceel.seal2020.

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The Sobrato Early Academic Language (SEAL) Model Research and Evaluation Final Report is comprised of three sets of studies that took place between 2015 and 2019 to examine the effectiveness of the SEAL Model in 67 schools within 12 districts across the state of California. Over a decade ago, the Sobrato Family Foundation responded to the enduring opportunity gaps and low academic outcomes for the state’s 1.2 million English Learners by investing in the design of the SEAL Model. The SEAL PreK–Grade 3 Model was created as a whole-school initiative to develop students’ language, literacy, and academic skills. The pilot study revealed promising findings, and the large-scale implementation of SEAL was launched in 2013. This report addresses a set of research questions and corresponding studies focused on: 1) the perceptions of school and district-level leaders regarding district and school site implementation of the SEAL Model, 2) teachers’ development and practices, and 3) student outcomes. The report is organized in five sections, within which are twelve research briefs that address the three areas of study. Technical appendices are included in each major section. A developmental evaluation process with mixed methods research design was used to answer the research questions. Key findings indicate that the implementation of the SEAL Model has taken root in many schools and districts where there is evidence of systemic efforts or instructional improvement for the English Learners they serve. In regards to teachers’ development and practices, there were statistically significant increases in the use of research-based practices for English Learners. Teachers indicated a greater sense of efficacy in addressing the needs of this population and believe the model has had a positive impact on their knowledge and skills to support the language and literacy development of PreK- Grade 3 English Learners. Student outcome data reveal that despite SEAL schools averaging higher rates of poverty compared to the statewide rate, SEAL English Learners in grades 2–4 performed comparably or better than California English Learners in developing their English proficiency; additional findings show that an overwhelming majority of SEAL students are rapidly progressing towards proficiency thus preventing them from becoming long-term English Learners. English Learners in bilingual programs advanced in their development of Spanish, while other English Learners suffered from language loss in Spanish. The final section of the report provides considerations and implications for further SEAL replication, sustainability, additional research and policy.
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EIA publications manual: Statistical graphs. Office of Scientific and Technical Information (OSTI), April 1985. http://dx.doi.org/10.2172/6118047.

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