Relevant bibliographies by topics / Graph-based analysis

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Graph-based analysis'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Graph-based analysis"

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Bhosale, Bharat. "Curvelet Based Multiresolution Analysis of Graph Neural Networks." International Journal of Applied Physics and Mathematics 4, no. 5 (2014): 313–23. http://dx.doi.org/10.7763/ijapm.2014.v4.304.

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Weghenkel, Björn, Asja Fischer, and Laurenz Wiskott. "Graph-based predictable feature analysis." Machine Learning 106, no. 9-10 (May 9, 2017): 1359–80. http://dx.doi.org/10.1007/s10994-017-5632-x.

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Micheloyannis, Sifis. "Graph-based network analysis in schizophrenia." World Journal of Psychiatry 2, no. 1 (2012): 1. http://dx.doi.org/10.5498/wjp.v2.i1.1.

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Coffman, Thayne, Seth Greenblatt, and Sherry Marcus. "Graph-based technologies for intelligence analysis." Communications of the ACM 47, no. 3 (March 2004): 45–47. http://dx.doi.org/10.1145/971617.971643.

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Stephen, Mutua, Changgui Gu, and Huijie Yang. "Visibility Graph Based Time Series Analysis." PLOS ONE 10, no. 11 (November 16, 2015): e0143015. http://dx.doi.org/10.1371/journal.pone.0143015.

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Perak, Benedikt, and Tajana Ban Kirigin. "Corpus-Based Syntactic-Semantic Graph Analysis." Rasprave Instituta za hrvatski jezik i jezikoslovlje 46, no. 2 (October 30, 2020): 957–96. http://dx.doi.org/10.31724/rihjj.46.2.27.

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This research exemplifies the corpus-based graph approach to the syntactic-semantic analysis of a concept feeling using the Construction Grammar Conceptual network methodology. by constructing a lexical network from grammatically tagged collocations of the english and the Croatian web corpora, the structure of the semantic domains is revealed as a set of sub-graphs derived from the source lexeme’s friend-of-a-friend graph. the subgraph structures, calculated with the community detection algorithm, are interpreted as the semantic domains associated with the source lexeme’s conceptual matrix. lexical structures are analyzed using a centrality algorithm that determines the overall rank of the salience and semantic relatedness to the source concept feeling. this empirical approach can be used for developing nlP methods and tasks, such as computing semantic similarity, sense disambiguation, sense structuring, as well as for comparative corpus and cross-cultural studies. ConGraCnet has a web application on the page <a target="_blank" rel="nofollow" href="http://emocnet.uniri.hr/congracnet">http://emocnet.uniri.hr/congracnet</a>.
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Qiang Luo, Quan Zhang, Mohsin Hafeez, Hua Xie, and Jie Li. "Graph-based Topology Analysis of Basin Structure." Journal of Convergence Information Technology 6, no. 5 (May 31, 2011): 245–50. http://dx.doi.org/10.4156/jcit.vol6.issue5.28.

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M. Dakhare, Kalyani. "Semantic Information Search Based on Graph Analysis." HELIX 8, no. 5 (August 31, 2018): 3927–31. http://dx.doi.org/10.29042/2018-3927-3931.

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Long, Seth, and Lawrence B. Holder. "Graph-Based Shape Analysis for MRI Classification." International Journal of Knowledge Discovery in Bioinformatics 2, no. 2 (April 2011): 19–33. http://dx.doi.org/10.4018/jkdb.2011040102.

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Searching for correlations between brain structure and attributes of a person’s intellectual state is a process which may be better done by automation than by human labor. Such an automated system would be capable of performing classification based on the discovered correlation, which would be means of testing how accurate the discovered correlation is. The authors have developed a system which generates a graph-based representation of the shape of the third and lateral ventricles based on a structural MRI, and classifies images represented in this manner. The system is evaluated on accuracy at classifying individuals showing cognitive impairment to Alzheimer’s Disease. Classification accuracy is 74.2% when individuals with CDR 0.5 are included as impaired in a balanced dataset of 166 images, and 79.3% accuracy when differentiating individuals with CDR at least 1.0 and healthy individuals in a balanced dataset of 54 images. Finally, the system is used to classify MR images according to level of education, with 77.2% accuracy differentiating highly-educated individuals from those for whom no higher education is listed, in a balanced dataset of 178 images.
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Caux, C., M. Barth, and R. De Guio. "Graph Based Tools for Production Flow Analysis." IFAC Proceedings Volumes 33, no. 17 (July 2000): 885–89. http://dx.doi.org/10.1016/s1474-6670(17)39520-4.

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Dissertations / Theses on the topic "Graph-based analysis"

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Schiller, Benjamin. "Graph-based Analysis of Dynamic Systems." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-230611.

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The analysis of dynamic systems provides insights into their time-dependent characteristics. This enables us to monitor, evaluate, and improve systems from various areas. They are often represented as graphs that model the system's components and their relations. The analysis of the resulting dynamic graphs yields great insights into the system's underlying structure, its characteristics, as well as properties of single components. The interpretation of these results can help us understand how a system works and how parameters influence its performance. This knowledge supports the design of new systems and the improvement of existing ones. The main issue in this scenario is the performance of analyzing the dynamic graph to obtain relevant properties. While various approaches have been developed to analyze dynamic graphs, it is not always clear which one performs best for the analysis of a specific graph. The runtime also depends on many other factors, including the size and topology of the graph, the frequency of changes, and the data structures used to represent the graph in memory. While the benefits and drawbacks of many data structures are well-known, their runtime is hard to predict when used for the representation of dynamic graphs. Hence, tools are required to benchmark and compare different algorithms for the computation of graph properties and data structures for the representation of dynamic graphs in memory. Based on deeper insights into their performance, new algorithms can be developed and efficient data structures can be selected. In this thesis, we present four contributions to tackle these problems: A benchmarking framework for dynamic graph analysis, novel algorithms for the efficient analysis of dynamic graphs, an approach for the parallelization of dynamic graph analysis, and a novel paradigm to select and adapt graph data structures. In addition, we present three use cases from the areas of social, computer, and biological networks to illustrate the great insights provided by their graph-based analysis. We present a new benchmarking framework for the analysis of dynamic graphs, the Dynamic Network Analyzer (DNA). It provides tools to benchmark and compare different algorithms for the analysis of dynamic graphs as well as the data structures used to represent them in memory. DNA supports the development of new algorithms and the automatic verification of their results. Its visualization component provides different ways to represent dynamic graphs and the results of their analysis. We introduce three new stream-based algorithms for the analysis of dynamic graphs. We evaluate their performance on synthetic as well as real-world dynamic graphs and compare their runtimes to snapshot-based algorithms. Our results show great performance gains for all three algorithms. The new stream-based algorithm StreaM_k, which counts the frequencies of k-vertex motifs, achieves speedups up to 19,043 x for synthetic and 2882 x for real-world datasets. We present a novel approach for the distributed processing of dynamic graphs, called parallel Dynamic Graph Analysis (pDNA). To analyze a dynamic graph, the work is distributed by a partitioner that creates subgraphs and assigns them to workers. They compute the properties of their respective subgraph using standard algorithms. Their results are used by the collator component to merge them to the properties of the original graph. We evaluate the performance of pDNA for the computation of five graph properties on two real-world dynamic graphs with up to 32 workers. Our approach achieves great speedups, especially for the analysis of complex graph measures. We introduce two novel approaches for the selection of efficient graph data structures. The compile-time approach estimates the workload of an analysis after an initial profiling phase and recommends efficient data structures based on benchmarking results. It achieves speedups of up to 5.4 x over baseline data structure configurations for the analysis of real-word dynamic graphs. The run-time approach monitors the workload during analysis and exchanges the graph representation if it finds a configuration that promises to be more efficient for the current workload. Compared to baseline configurations, it achieves speedups up to 7.3 x for the analysis of a synthetic workload. Our contributions provide novel approaches for the efficient analysis of dynamic graphs and tools to further investigate the trade-offs between different factors that influence the performance.
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Huang, Zan. "GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.

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Corazza, Federico Augusto. "Analysis of graph-based quantum error-correcting codes." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23801/.

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With the advent of quantum computers, there has been a growing interest in the practicality of this device. Due to the delicate conditions that surround physical qubits, one could wonder whether any useful computation could be implemented on such devices. As we describe in this work, it is possible to exploit concepts from classical information theory and employ quantum error-correcting techniques. Thanks to the Threshold Theorem, if the error probability of physical qubits is below a given threshold, then the logical error probability corresponding to the encoded data qubit can be arbitrarily low. To this end, we describe decoherence which is the phenomenon that quantum bits are subject to and is the main source of errors in quantum memories. From the cause of error of a single qubit, we then introduce the error models that can be used to analyze quantum error-correcting codes as a whole. The main type of code that we studied comes from the family of topological codes and is called surface code. Of these codes, we consider both the toric and planar structures. We then introduce a variation of the standard planar surface code which better captures the symmetries of the code architecture. Once the main properties of surface codes have been discussed, we give an overview of the working principles of the algorithm used to decode this type of topological code: the minimum weight perfect matching. Finally, we show the performance of the surface codes that we introduced, comparing them based on their architecture and properties. These simulations have been performed with different error channel models to give a more thorough description of their performance in several situations showing relevant results.
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Rahman, Md Rashedur. "Knowledge Base Population based on Entity Graph Analysis." Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS092/document.

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Le peuplement de base de connaissance (KBP) est une tâche importante qui présente de nombreux défis pour le traitement automatique des langues. L'objectif de cette tâche est d'extraire des connaissances de textes et de les structurer afin de compléter une base de connaissances. Nous nous sommes intéressés à la reconnaissance de relations entre entités. L'extraction de relations (RE) entre une paire de mentions d'entités est une tâche difficile en particulier pour les relations en domaine ouvert. Généralement, ces relations sont extraites en fonction des informations lexicales et syntaxiques au niveau de la phrase. Cependant, l'exploitation d'informations globales sur les entités n'a pas encore été explorée. Nous proposons d'extraire un graphe d'entités du corpus global et de calculer des caractéristiques sur ce graphe afin de capturer des indices des relations entre paires d'entités. Pour évaluer la pertinence des fonctionnalités proposées, nous les avons testées sur une tâche de validation de relation dont le but est de décider l'exactitude de relations extraites par différents systèmes. Les résultats expérimentaux montrent que les caractéristiques proposées conduisent à améliorer les résultats de l'état de l'art
Knowledge Base Population (KBP) is an important and challenging task specially when it has to be done automatically. The objective of KBP task is to make a collection of facts of the world. A Knowledge Base (KB) contains different entities, relationships among them and various properties of the entities. Relation extraction (RE) between a pair of entity mentions from text plays a vital role in KBP task. RE is also a challenging task specially for open domain relations. Generally, relations are extracted based on the lexical and syntactical information at the sentence level. However, global information about known entities has not been explored yet for RE task. We propose to extract a graph of entities from the overall corpus and to compute features on this graph that are able to capture some evidence of holding relationships between a pair of entities. In order to evaluate the relevance of the proposed features, we tested them on a task of relation validation which examines the correctness of relations that are extracted by different RE systems. Experimental results show that the proposed features lead to outperforming the state-of-the-art system
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Fujimoto, Masaki Stanley. "Graph-Based Whole Genome Phylogenomics." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8461.

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Understanding others is a deeply human urge basic in our existential quest. It requires knowing where someone has come from and where they sit amongst peers. Phylogenetic analysis and genome wide association studies seek to tell us where we’ve come from and where we are relative to one another through evolutionary history and genetic makeup. Current methods do not address the computational complexity caused by new forms of genomic data, namely long-read DNA sequencing and increased abundances of assembled genomes, that are becoming evermore abundant. To address this, we explore specialized data structures for storing and comparing genomic information. This work resulted in the creation of novel data structures for storing multiple genomes that can be used for identifying structural variations and other types of polymorphisms. Using these methods we illuminate the genetic history of organisms in our efforts to understand the world around us.
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Wang, Kaijun. "Graph-based Modern Nonparametrics For High-dimensional Data." Diss., Temple University Libraries, 2019. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/578840.

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Statistics
Ph.D.
Developing nonparametric statistical methods and inference procedures for high-dimensional large data have been a challenging frontier problem of statistics. To attack this problem, in recent years, a clear rising trend has been observed with a radically different viewpoint--``Graph-based Nonparametrics," which is the main research focus of this dissertation. The basic idea consists of two steps: (i) representation step: code the given data using graphs, (ii) analysis step: apply statistical methods on the graph-transformed problem to systematically tackle various types of data structures. Under this general framework, this dissertation develops two major research directions. Chapter 2—based on Mukhopadhyay and Wang (2019a)—introduces a new nonparametric method for high-dimensional k-sample comparison problem that is distribution-free, robust, and continues to work even when the dimension of the data is larger than the sample size. The proposed theory is based on modern LP-nonparametrics tools and unexplored connections with spectral graph theory. The key is to construct a specially-designed weighted graph from the data and to reformulate the k-sample problem into a community detection problem. The procedure is shown to possess various desirable properties along with a characteristic exploratory flavor that has practical consequences. The numerical examples show surprisingly well performance of our method under a broad range of realistic situations. Chapter 3—based on Mukhopadhyay and Wang (2019b)—revisits some foundational questions about network modeling that are still unsolved. In particular, we present unified statistical theory of the fundamental spectral graph methods (e.g., Laplacian, Modularity, Diffusion map, regularized Laplacian, Google PageRank model), which are often viewed as spectral heuristic-based empirical mystery facts. Despite half a century of research, this question has been one of the most formidable open issues, if not the core problem in modern network science. Our approach integrates modern nonparametric statistics, mathematical approximation theory (of integral equations), and computational harmonic analysis in a novel way to develop a theory that unifies and generalizes the existing paradigm. From a practical standpoint, it is shown that this perspective can provide adequate guidance for designing next-generation computational tools for large-scale problems. As an example, we have described the high-dimensional change-point detection problem. Chapter 4 discusses some further extensions and application of our methodologies to regularized spectral clustering and spatial graph regression problems. The dissertation concludes with the a discussion of two important areas of future studies.
Temple University--Theses
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Sinha, Ravi Som Mihalcea Rada F. "Graph-based centrality algorithms for unsupervised word sense disambiguation." [Denton, Tex.] : University of North Texas, 2008. http://digital.library.unt.edu/permalink/meta-dc-9736.

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Malmberg, Filip. "Graph-based Methods for Interactive Image Segmentation." Doctoral thesis, Uppsala universitet, Centrum för bildanalys, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-149261.

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The subject of digital image analysis deals with extracting relevant information from image data, stored in digital form in a computer. A fundamental problem in image analysis is image segmentation, i.e., the identification and separation of relevant objects and structures in an image. Accurate segmentation of objects of interest is often required before further processing and analysis can be performed. Despite years of active research, fully automatic segmentation of arbitrary images remains an unsolved problem. Interactive, or semi-automatic, segmentation methods use human expert knowledge as additional input, thereby making the segmentation problem more tractable. The goal of interactive segmentation methods is to minimize the required user interaction time, while maintaining tight user control to guarantee the correctness of the results. Methods for interactive segmentation typically operate under one of two paradigms for user guidance: (1) Specification of pieces of the boundary of the desired object(s). (2) Specification of correct segmentation labels for a small subset of the image elements. These types of user input are referred to as boundary constraints and regional constraints, respectively. This thesis concerns the development of methods for interactive segmentation, using a graph-theoretic approach. We view an image as an edge weighted graph, whose vertex set is the set of image elements, and whose edges are given by an adjacency relation among the image elements. Due to its discrete nature and mathematical simplicity, this graph based image representation lends itself well to the development of efficient, and provably correct, methods. The contributions in this thesis may be summarized as follows: Existing graph-based methods for interactive segmentation are modified to improve their performance on images with noisy or missing data, while maintaining a low computational cost. Fuzzy techniques are utilized to obtain segmentations from which feature measurements can be made with increased precision. A new paradigm for user guidance, that unifies and generalizes regional and boundary constraints, is proposed. The practical utility of the proposed methods is illustrated with examples from the medical field.
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Durai, Dilip [Verfasser]. "Novel graph based algorithms for transcriptome sequence analysis / Dilip Durai." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2020. http://d-nb.info/1236897064/34.

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Gong, Nan. "Using Map-Reduce for Large Scale Analysis of Graph-Based Data." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-102822.

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As social networks have gained in popularity, maintaining and processing the social network graph information using graph algorithms has become an essential source for discovering potential features of the graph. The escalating size of the social networks has made it impossible to process the huge graphs on a single ma chine in a “real-time” level of execution. This thesis is looking into representing and distributing graph-based algorithms using Map-Reduce model. Graph-based algorithms are discussed in the beginning. Then, several distributed graph computing infrastructures are reviewed, followed by Map-Reduce introduction and some graph computation toolkits based on Map-Reduce model. By reviewing the background and related work, graph-based algorithms are categorized, and adaptation of graph-based algorithms to Map-Reduce model is discussed. Two particular algorithms, MCL and DBSCAN are chosen to be designed using Map- Reduce model, and implemented using Hadoop. New matrix multiplication method is proposed while designing MCL. The DBSCAN is reformulated into connectivity problem using filter method, and Kingdom Expansion Game is proposed to do fast expansion. Scalability and performance of these new designs are evaluated. Conclusion is made according to the literature study, practical design experience and evaluation data. Some suggestions of graph-based algorithms design using Map-Reduce model are also given in the end.
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Books on the topic "Graph-based analysis"

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Kanjilal, Ananya. Graph based analysis of object oriented systems: An integrated approach. New York: Nova Science Publishers, 2010.

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Sabetzadeh, Mehrdad. A category-theoretic approach to representation and analysis of inconsistency in graph-based viewpoints. Ottawa: National Library of Canada, 2003.

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International Seminar on Random Graphs and Probabilistic Methods in Combinatorics (3rd 1987 Poznaṅ, Poland). Random graphs '87: Based on proceedings of the 3rd International Seminar on Random Graphs and Probabilistic Methods in Combinatorics, June 27 - July 3 1987, Poznaṅ, Poland. Chichester [England]: Wiley, 1990.

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Weighted graph based ordering techniques for preconditioned conjugate gradient methods. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

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Coolen, Ton, Alessia Annibale, and Ekaterina Roberts. Generating Random Networks and Graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.001.0001.

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This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation, as well as discussions of the main strengths and weaknesses of each approach. It includes extensive references for readers wishing to go further. The material is carefully structured to be accessible to researchers from all disciplines while also containing rigorous mathematical analysis (largely based on the techniques of statistical mechanics) to support those wishing to further develop or implement the theory of random graph generation. This book is aimed at the graduate student or advanced undergraduate. It includes many worked examples, numerical simulations and exercises making it suitable for use in teaching. Explicit pseudocode algorithms are included to make the ideas easy to apply. Datasets are becoming increasingly large and network applications wider and more sophisticated. Testing hypotheses against properly specified control cases (null models) is at the heart of the ‘scientific method’. Knowledge on how to generate controlled and unbiased random graph ensembles is vital for anybody wishing to apply network science in their research.
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Practical R for biologists: an introduction. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789245349.0000.

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Abstract R is an open-source statistical environment modelled after the previously widely used commercial programs S and S-Plus, but in addition to powerful statistical analysis tools, it also provides powerful graphics outputs. In addition to its statistical and graphical capabilities, R is a programming language suitable for medium-sized projects. This book presents a set of studies that collectively represent almost all the R operations that beginners, analysing their own data up to perhaps the early years of doing a PhD, need. Although the chapters are organized around topics such as graphing, classical statistical tests, statistical modelling, mapping and text parsing, examples have been chosen based largely on real scientific studies at the appropriate level and within each the use of more R functions is nearly always covered than are simply necessary just to get a p-value or a graph. R comes with around a thousand base functions which are automatically installed when R is downloaded. This book covers the use of those of most relevance to biological data analysis, modelling and graphics. Throughout each chapter, the functions introduced and used in that chapter are summarized in Tool Boxes. The book also shows the user how to adapt and write their own code and functions. A selection of base functions relevant to graphics that are not necessarily covered in the main text are described in Appendix 1, and additional housekeeping functions in Appendix 2.
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Book chapters on the topic "Graph-based analysis"

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Galvão, Ismênia, Eduardo Zambon, Arend Rensink, Lesley Wevers, and Mehmet Aksit. "Knowledge-Based Graph Exploration Analysis." In Applications of Graph Transformations with Industrial Relevance, 105–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34176-2_11.

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Priss, Uta. "Applying Semiotic-Conceptual Analysis to Mathematical Language." In Graph-Based Representation and Reasoning, 248–56. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23182-8_19.

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Priss, Uta. "A Semiotic-Conceptual Analysis of Conceptual Learning." In Graph-Based Representation and Reasoning, 122–36. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_10.

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Priss, Uta, and L. John Old. "Semiotic-Conceptual Analysis of a Lexical Field." In Graph-Based Representation and Reasoning, 239–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23182-8_18.

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Rodrigues, Franciele, Pedro Leal, Yukiko Kenmochi, Jean Cousty, Laurent Najman, Silvio Guimarães, and Zenilton Patrocínio. "Graph-Based Hierarchical Video Cosegmentation." In Image Analysis and Processing - ICIAP 2017, 15–26. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68560-1_2.

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Xia, Shengping, and Edwin R. Hancock. "Graph-Based Object Class Discovery." In Computer Analysis of Images and Patterns, 385–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03767-2_47.

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Sokolsky, Oleg, Sampath Kannan, and Insup Lee. "Simulation-Based Graph Similarity." In Tools and Algorithms for the Construction and Analysis of Systems, 426–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11691372_28.

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Helden, J., L. Wernisch, D. Gilbert, and S. J. Wodak. "Graph-Based Analysis of Metabolic Networks." In Bioinformatics and Genome Analysis, 245–74. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-04747-7_12.

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Banerjee, J., and J. McPhee. "Graph-Theoretic Modelling and Sensitivity Analysis of Dynamic Systems." In Graph-Based Modelling in Engineering, 107–16. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39020-8_8.

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Moreno, G. G., R. L. P. Barreto, R. S. Vieira, L. Nicolazzi, and D. Martins. "Three-Dimensional Analysis of Vehicle Stability Using Graph Theory." In Graph-Based Modelling in Engineering, 117–29. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39020-8_9.

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Conference papers on the topic "Graph-based analysis"

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Matveeva, Irina, and Gina-Anne Levow. "Graph-based generalized latent semantic analysis for document representation." In TextGraphs: the First Workshop on Graph Based Methods for Natural Language Processing. Morristown, NJ, USA: Association for Computational Linguistics, 2006. http://dx.doi.org/10.3115/1654758.1654771.

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Левченков, Александр Николаевич, and Эльвира Наджаф кызы Абдуллаева. "GRAPH-BASED SECURITY RISK ANALYSIS." In Национальная безопасность России: актуальные аспекты: сборник избранных статей Всероссийской научно-практической конференции (Санкт-Петербург, Май 2020). Crossref, 2020. http://dx.doi.org/10.37539/nb185.2020.68.59.009.

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В статье описывается один из подходов к анализу угроз безопасности информации на основе использования графовой модели, которая описывает различные информационные потоки на основе учета политики безопасности, которая определяет правила взаимодействия информационных потоков с информационными объектами и узлами с целью выявления и перекрытия несанкционированных маршрутов. The article describes one of the approaches to the analysis of information security threats based on the use of a graph model that describes various information flows based on a security policy that defines the rules for the interaction of information flows with information objects and nodes in order to identify and block unauthorized routes.
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Castelltort, Arnaud, and Anne Laurent. "NoSQL Graph-based OLAP Analysis." In International Conference on Knowledge Discovery and Information Retrieval. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0005072902170224.

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Le, Thanh-Nam, Muhammad Muzzamil Luqman, Jean-Christophe Burie, and Jean-Marc Ogier. "Content-based comic retrieval using multilayer graph representation and frequent graph mining." In 2015 13th International Conference on Document Analysis and Recognition (ICDAR). IEEE, 2015. http://dx.doi.org/10.1109/icdar.2015.7333864.

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Dalmia, Ayushi, Manish Gupta, and Vasudeva Varma. "Query-based Graph Cuboid Outlier Detection." In ASONAM '15: Advances in Social Networks Analysis and Mining 2015. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2808797.2810061.

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Sakarya, Ufuk, and Ziya Telatar. "Graph Partition Based Scene Boundary Detection." In 2007 5th International Symposium on Image and Signal Processing and Analysis. IEEE, 2007. http://dx.doi.org/10.1109/ispa.2007.4383752.

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Gupta, Upa, and Leonidas Fegaras. "Map-based graph analysis on MapReduce." In 2013 IEEE International Conference on Big Data. IEEE, 2013. http://dx.doi.org/10.1109/bigdata.2013.6691550.

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Collins, M. Patrick. "Graph-based analysis in network security." In MILCOM 2011 - 2011 IEEE Military Communications Conference. IEEE, 2011. http://dx.doi.org/10.1109/milcom.2011.6127488.

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Ammann, Paul, Duminda Wijesekera, and Saket Kaushik. "Scalable, graph-based network vulnerability analysis." In the 9th ACM conference. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/586110.586140.

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Kolka, Zdenek, Martin Vlk, Dalibor Biolek, and Viera Biolkova. "Symbolic Analysis Based on Graph Transformations." In 2007 14th IEEE International Conference on Electronics, Circuits and Systems (ICECS '07). IEEE, 2007. http://dx.doi.org/10.1109/icecs.2007.4511196.

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Reports on the topic "Graph-based analysis"

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Swiler, L. P., C. Phillips, and T. Gaylor. A graph-based network-vulnerability analysis system. Office of Scientific and Technical Information (OSTI), January 1998. http://dx.doi.org/10.2172/573291.

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Mayo, Jackson R., W. Philip, Jr Kegelmeyer, Matthew H. Wong, Philippe Pierre Pebay, Ann C. Gentile, David C. Thompson, Diana C. Roe, Vincent De Sapio, and James M. Brandt. A framework for graph-based synthesis, analysis, and visualization of HPC cluster job data. Office of Scientific and Technical Information (OSTI), August 2010. http://dx.doi.org/10.2172/992310.

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Aadithya, Karthik, Eric Keiter, and Ting Mei. DAGSENS: Directed Acyclic Graph Based Direct and Adjoint Transient Sensitivity Analysis for Event-Driven Objective Functions. Office of Scientific and Technical Information (OSTI), August 2017. http://dx.doi.org/10.2172/1761808.

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Monetary Policy Report - January 2021. Banco de la República de Colombia, March 2021. http://dx.doi.org/10.32468/inf-pol-mont-eng.tr1.-2021.

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Macroeconomic Summary Overall inflation (1.61%) and core inflation (excluding food and regulated items) (1.11%) both declined beyond the technical staff’s expectations in the fourth quarter of 2020. Year-end 2021 forecasts for both indicators were revised downward to 2.3% and 2.1%, respectively. Market inflation expectations also fell over this period and suggested inflation below the 3% target through the end of this year, rising to the target in 2022. Downward pressure on inflation was more significant in the fourth quarter than previously projected, indicating weak demand. Annual deceleration among the main groups of the consumer price index (CPI) was generalized and, except for foods, was greater than projected in the October report. The CPI for goods (excluding foods and regulated items) and the CPI for regulated items were subject to the largest decelerations and forecasting discrepancies. In the first case, this was due in part to a greater-than-expected effect on prices from the government’s “VAT-fee day” amid weak demand, and from the extension of some price relief measures. For regulated items, the deceleration was caused in part by unanticipated declines in some utility prices. Annual change in the CPI for services continued to decline as a result of the performance of those services that were not subject to price relief measures, in particular. Although some of the overall decline in inflation is expected to be temporary and reverse course in the second quarter of 2021, various sources of downward pressure on inflation have become more acute and will likely remain into next year. These include ample excesses in capacity, as suggested by the continued and greater-than-expected deceleration in core inflation indicators and in the CPI for services excluding price relief measures. This dynamic is also suggested by the minimal transmission of accumulated depreciation of the peso on domestic prices. Although excess capacity should fall in 2021, the decline will likely be slower than projected in the October report amid additional restrictions on mobility due to a recent acceleration of growth in COVID-19 cases. An additional factor is that low inflation registered at the end of 2020 will likely be reflected in low price adjustments on certain indexed services with significant weight in the CPI, including real estate rentals and some utilities. These factors should keep inflation below the target and lower than estimates from the previous report on the forecast horizon. Inflation is expected to continue to decline to levels near 1% in March, later increasing to 2.3% at the end of 2021 and 2.7% at year-end 2022 (Graph 1.1). According to the Bank’s most recent survey, market analysts expect inflation of 2.7% and 3.1% in December 2021 and 2022, respectively. Expected inflation derived from government bonds was 2% for year-end 2021, while expected inflation based on bonds one year forward from that date (FBEI 1-1 2022) was 3.2%.
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