Relevant bibliographies by topics / Prediction theory Graphic methods

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

Academic literature on the topic 'Prediction theory Graphic methods'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Prediction theory Graphic methods"

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Klodāne, Aļona, Anda Zvaigzne, Karlis Poznakovs, and Andris Pelšs. "Theoretical Aspects of Modern Qualitative Methods for Crisis Management." Latgale National Economy Research 1, no. 11 (October 15, 2019): 39. http://dx.doi.org/10.17770/lner2019vol1.11.4366.

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Scientists agree on necessity to predict and timely detect a company crisis that is an integral part of business nowadays. The early identification of a company financial failure due to rapidly changing social, economic and environmental conditions is a topic of growing importance. The sooner a potential financial failure can be identified, the more effective and efficient the anti-crisis measures could be implemented. If a company financial failure could be predicted accurately, it might be possible for the business to be restructured, thus avoiding the failure. This would benefit the owners, shareholders, employees, creditors, and others interested alike. The modern economic literature provides varied crisis diagnosis methods, mostly based on an analysis of key financial ratios. Less attention is paid to qualitative methods. Therefore, the present research aims to examine modern qualitative methods for company crisis diagnosis (prediction) in crisis management theory. The research used the theoretical findings of foreign scientists and Internet resources. The research employed the following methods: analysis and synthesis, induction and deduction, the monographic method, document analysis and the graphic method. The paper provides definitions of main terms related to company failure prediction and a description of general qualitative models of company financial failure (bankruptcy) prediction.
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Et. al., R. Divya Mounika,. "A Benchmarking application on Workload and Performance forecasting of micro services." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (April 10, 2021): 3232–38. http://dx.doi.org/10.17762/turcomat.v12i2.2381.

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Micro services are increasingly understood as the ideal architectural framework for building large cloud applications within and beyond organizational boundaries. These micro services architectures scale up the application, but are expensive to work on, so pay attention to workflow planning and workflow planning. However, this issue is not very clear. In this work, we are developing independent micro services workflows suitable for modeling and prediction methods and designing three-step game models for based applications. Solved the problem of designing micro services based applications to reduce end-to-end delays under user-specific limitations (MAWS-BC) and recommended micro services routing algorithms. The design process and estimation methods are improved and adequate. The experimental results produced by a well-known micro service bank cover a wide variety of statistical analyzes and the production utility of graphic design is shown by a large comparison copy compared to current algorithms.
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Ross, Gordon. "Airlines, Mayonnaise, and Justice: Reflections on the Theory and Practice of Legal Design and Technology." Design Issues 36, no. 3 (June 2020): 31–44. http://dx.doi.org/10.1162/desi_a_00602.

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Design theory and practice are increasingly prevalent in legal and justice systems, at times reframing the relationship between the law and the world it seeks to make just. This article presents my professional experiences introducing design theory, methods, and mindsets into legal and government contexts, highlighting challenges and tensions across various legal design projects. Challenges include the justice sector's struggles dealing with complexity, the unfamiliar logics of design and abductive reasoning, and the legacy of positivist and instrumentalist philosophies. As a response, I propose a legal design that incorporates phronesis and re-orients around anticipation, rather than prediction.
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Syshchuk, Andrii, and Iryna Panasiuk. "HISTORY AND PRACTICE OF TECHNICAL METHODS OF THE INTERNATIONAL CURRENCY MARKET." Economic journal of Lesia Ukrainka Eastern European National University 1, no. 21 (March 30, 2020): 12–21. http://dx.doi.org/10.29038/2411-4014-2020-01-12-21.

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Introduction. Technical analysis is an assessment of the behavior of the international currency market over a period of time. Due to the unpredictability of the dynamics of the international currency market and the possibility of losses from the conducted transactions, the study of technical means becomes of particular importance and relevance. It is the comparative analysis that identifies the advantages and disadvantages of each of the methods in order to further formulate the most profitable trading strategy. The purpose of the article is a comparative analysis of the technical analysis methods used by analysts in today's international currency market; comparing the selected instruments of each method and determining the most effective ones. Results. Within the framework of this study three main methods of technical analysis of the international currency market were considered: graphic, method of mathematical approximation and theory of economic cycles. The individual instruments of each of the methods, such as «Japanese candlestick», simple moving average, the moving average convergence divergence (MACD), MACD histogram, Elliott waves, Fibonacci retracement levels are reviewed. A comparative analysis of the selected instruments is carried out on the basis of research of the specificity of each of them. Examples of graphs, indicators and histograms are given. It is identified that the main differences are the complexity of use (ie the use of mathematical computing and computer technology) and the type of strategy (short or long term). Common features of methods of technical analysis of the currency market are: the purpose of the analysis, the object of analysis and the influence of the factor «psychology of people». Conclusions. It is revealed that one can obtain the most accurate results in predicting the dynamics of currency quotations only by combining several methods simultaneously. Knowing the strengths and weaknesses of different technical analysis tools, you can use them to validate each other's signals. As a result of such tactics, the analyst will get more accurate indicators that will bring him a profit. The prospect of the research is to find the best strategic schemes using a wide range of technical tools for international currency market analysis.
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Kostin, M. O., A. M. Mukha, O. H. Sheikina, and O. Y. Kurylenko. "Determination of Energy and Electric Capacity of On-Board Supercapaci-tor Regenerative Energy Storage." Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport, no. 2(92) (April 15, 2021): 29–39. http://dx.doi.org/10.15802/stp2021/237500.

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Purpose. Development of a method for determining the main functional parameters of on-board supercapacitor recuperative energy storage based on the asymptotic theory of extreme statistics by Gumbel, taking into account stochastic nature of changes in recuperated voltage and current. Methodology. To achieve this purpose, methods, devices and computer systems for temporary registration of recuperated voltages and currents on operating electric locomotives, methods of the theory of random processes and methods of probabilistic and statistical processing of registrograms of voltages and currents were used. Findings. A computational and experimental method for estimating recuperative energy has been proposed and practically applied. A probabilistic method has been developed for determining the energy and electric capacity of on-board supercapacitor recuperative energy storage units. Numerical probabilistic and statistical calculations of the energy and electric capacity of on-board storage for the VL8 and VL11M6 electric locomotives during their operation in the sections of Prydniprovska railway have been carried out. It was found that the energy and electric capacity of on-board storage devices are distributed according to an exponential law with a clear prevalence of their minimum values and in compliance with direct proportionality between them. Originality. For the first time, an autonomous phase of recuperative braking mode of an electric rolling stock has been developed, which makes it possible to significantly reduce the mass and dimension of a supercapacitor storage. The asymptotic theory of extreme statistics by Gumbel was adapted to the method for calculating energy and electric capacity of an on-board storage device, which made it possible to take into account the influence of stochastic nature of changes in the recuperated voltage and current. The probabilistic influence regularities of the change nature in the recuperation energy on the capacity of on-board storage in the phase of recuperative braking have been established. Further development was obtained by a computational-experimental method for assessing the recuperative energy, based on monitoring and using the time dependences of voltage and current obtained in real modes of recuperative braking. For the first time in electric traction systems, it was proposed to carry out the transition from the recuperative braking mode to the recuperative regeneration mode. Practical value. The developed method and technique based on it make it possible to evaluate functional parameters of on-board storage device of all types of electric rolling stock, considering stochastic nature of recuperated voltages and currents. Numerical-graphic dependences of the energy intensity and capacity of the on-board storage are recommended for predicting and evaluating these parameters for various modes of recuperative braking. Since the task of designing an on-board storage unit (in terms of mass and dimensions) is ambiguous, therefore, in each specific case of the type of electric locomotive and recuperation modes, it must be solved individually, taking into account the probability of the corresponding capacitance values.
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Fralenko, Vitaly, Vyacheslav Khachumov, and Mikhail Khachumov. "Correlation analysis and prediction of personality traits using graphic data collections." SHS Web of Conferences 72 (2019): 01012. http://dx.doi.org/10.1051/shsconf/20197201012.

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The questions of building mechanisms for identifying patterns and building modern tools for analyzing data from social networks are considered. It is proposed to apply modern methods of web pages’ automatic analysis, testing hypotheses about the presence of correlation links, automatic classification of graphic information using the apparatus of artificial neural networks. The presence of correlation between personality traits of the "Big Five" is investigated. Strong fluctuations in the values of personality traits were revealed depending on various types for groups of people. The problem of predicting the personality traits of the Internet user by the images posted by him is investigated, artificial neural networks are used as a tool. Two series of experiments were carried out, in the first series, a convolutional neural network, trained on the images and results of the NEO-FFI questionnaire, was used to predict personality traits. The sequential use of convolution and subsampling in the convolution network leads to the so-called increase in the level of features: if the first layer extracts local features from the image, then subsequent layers extract common features that are called high-order features. In the second series of experiments, this type of artificial neural network was used to extract high-level features, which were then used to train a direct distribution network that performs forecasting. Thus, the more layers are used, the more features associated with personality traits are extracted from the images. For processing arrays of graphic information, the “Microsoft Cognitive Toolkit library” and the Nvidia Geforce GTX 1080 Ti graphics accelerator were used. The results of the experiments revealed those personality traits that are most correlated with the images posted by Internet users.
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Харах, Matvey Kharakh, Козлова, Irina Kozlova, Славин, Boris Slavin, Славин, R. Slavin, Гусева, and T. Guseva. "Parameterization Theory Elements and Parametric Drawings in "KOMPAS-Graphic"." Geometry & Graphics 5, no. 1 (April 17, 2017): 64–72. http://dx.doi.org/10.12737/25125.

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Some elements of geometric objects’ parameterization theory are considered in this paper. An analysis of this theory development by leading Soviet scientists has been presented. It is known that figure’s parameters are determined by adding shape parameters with location parameters. In the drawing these parameters are expressed as dimensions. Relations between figures are determined or by their mutual affiliation, or by perpendicularity, or by their touch. These ratios allow reduce dimensions number on a drawing. The link of the parameterization theory with creation of parametric drawings has been demonstrated in the considering work. Definitions for such concepts as parameter and parametric drawing have been given. A various geometric figures’ parameters calculation principle has been considered. The order of assignment in parametric form of different geometrical objects (point, straight line), as well as any figure’s position in space has been presented. Assignments of geometrical conditions for membership, parallelism, perpendicularity, contact by parameters have been considered. A detailed description for parametric drawing in "KOMPAS-Graphic" program has been presented. Advantages and features related to carrying out of parametric fragments (with geometric and dimensional dependencies) and drawings in this program for various industries have been laid in detail. Some features and approaches in carrying out parametric drawings and fragments, which worthwhile to apply for different modifications of items when needed to creat group drawings, have been proposed for consideration. Traditional methods for creation of the group drawings using CAD systems effectively increase intellectual work’s performance. The CAD systems capabilities use will enable designers to implement various ways and possibilities comprehensively for creation and presentation of graphic information.
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Nørgaard, L., A. Saudland, J. Wagner, J. P. Nielsen, L. Munck, and S. B. Engelsen. "Interval Partial Least-Squares Regression (iPLS): A Comparative Chemometric Study with an Example from Near-Infrared Spectroscopy." Applied Spectroscopy 54, no. 3 (March 2000): 413–19. http://dx.doi.org/10.1366/0003702001949500.

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A new graphically oriented local modeling procedure called interval partial least-squares ( iPLS) is presented for use on spectral data. The iPLS method is compared to full-spectrum partial least-squares and the variable selection methods principal variables (PV), forward stepwise selection (FSS), and recursively weighted regression (RWR). The methods are tested on a near-infrared (NIR) spectral data set recorded on 60 beer samples correlated to original extract concentration. The error of the full-spectrum correlation model between NIR and original extract concentration was reduced by a factor of 4 with the use of iPLS ( r = 0.998, and root mean square error of prediction equal to 0.17% plato), and the graphic output contributed to the interpretation of the chemical system under observation. The other methods tested gave a comparable reduction in the prediction error but suffered from the interpretation advantage of the graphic interface. The intervals chosen by iPLS cover both the variables found by FSS and all possible combinations as well as the variables found by PV and RWR, and iPLS is still able to utilize the first-order advantage.
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Evans, Susan M., Susan L. Palmiter, and Jay Elkerton. "The Edge System: Ergonomic Design Using Graphic Evaluation." Proceedings of the Human Factors Society Annual Meeting 32, no. 11 (October 1988): 612–16. http://dx.doi.org/10.1518/107118188786762441.

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EDGE is a computer-based ergonomic workspace design system which integrates several models of operator performance with a common graphic interface. In addition to serving as a practical design system, it also serves as a research tool for understanding the ergonomic design process in industry. System users include trained ergonomists and engineers responsible for manual workspace design. The design system centers around a core vocabulary of task-related terms. A common input format, modeled after the traditional “work methods table” addresses the input requirements of the varied human performance models. Output from the performance models is displayed on multiple screens in varying levels of detail. Among the measures of physical stress currently integrated into the system are models of biomechanical strength, NIOSH lifting limits, metabolic energy expenditure, and elemental time prediction.
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Saltini, L. E., and A. Zadra. "Algebra of Nonlocal Charges in Supersymmetric Nonlinear Sigma Models." International Journal of Modern Physics A 12, no. 02 (January 20, 1997): 419–36. http://dx.doi.org/10.1142/s0217751x97000487.

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We propose a graphic method to derive the classical algebra (Dirac brackets) of nonlocal conserved charges in the two-dimensional supersymmetric nonlinear O(N) sigma model. As in the purely bosonic theory we find a cubic Yangian algebra. We also consider the extension of graphic methods to other integrable theories.
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Dissertations / Theses on the topic "Prediction theory Graphic methods"

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Ketkar, Nikhil S. "Empirical comparison of graph classification and regression algorithms." Pullman, Wash. : Washington State University, 2009. http://www.dissertations.wsu.edu/Dissertations/Spring2009/n_ketkar_042409.pdf.

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Thesis (Ph. D.)--Washington State University, May 2009.
Title from PDF title page (viewed on June 3, 2009). "School of Electrical Engineering and Computer Science." Includes bibliographical references (p. 101-108).
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Corbett, Dan R. "Unification and constraints over conceptual structures." Title page, contents and summary only, 2000. http://web4.library.adelaide.edu.au/theses/09PH/09phc7889.pdf.

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Bibliography: leaves 150-161. This thesis addresses two areas in the field of conceptual structures. The first is the unification of conceptual graphs, and the consequent work in projection and in type hierarchies... The second area of investigation is the definition of constraints, especially real-value constraints on the concept referents, with particular attention to handling constraints during the unification of conceptual graphs.
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Abras, Jennifer N. "Enhancement of aeroelastic rotor airload prediction methods." Diss., Atlanta, Ga. : Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/28182.

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Thesis (M. S.)--Aerospace Engineering, Georgia Institute of Technology, 2009.
Committee Chair: Smith, Marilyn; Committee Member: Bauchau, Olivier; Committee Member: Costello, Mark; Committee Member: Moulton, Marvin; Committee Member: Ruffin, Stephen.
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Dey, Sanjoy. "Structural properties of visibility and weak visibility graphs." Virtual Press, 1997. http://liblink.bsu.edu/uhtbin/catkey/1048394.

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Given a finite set S of n nonintersecting line segments with no three end points collinear, the segment end point visibility graph is defined as the graph whose vertices are the end points of the line segments in S and two vertices are adjacent if the straight line segment joining two end points does not intersect any element of S, or if they are end points of the same segment. Segment end point visibility graphs have a wide variety of applications in VLSI circuit design, study of art gallery problems, and other areas of computational geometry. This thesis contains a survey of the important results that are currently known regarding the characterization of these graphs. Also a weak visibility dual of a segment end point visibility graph is defined and some structural properties of such graphs are presented. Some open problems and questions related to the characterization of weak visibility graphs are also discussed.
Department of Mathematical Sciences
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馮榮錦 and Wing-kam Tony Fung. "Analysis of outliers using graphical and quasi-Bayesian methods." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31230842.

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Khouzam, Nelly. "A new class of brittle graphs /." Thesis, McGill University, 1986. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66048.

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Armstrong, Helen School of Mathematics UNSW. "Bayesian estimation of decomposable Gaussian graphical models." Awarded by:University of New South Wales. School of Mathematics, 2005. http://handle.unsw.edu.au/1959.4/24295.

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This thesis explains to statisticians what graphical models are and how to use them for statistical inference; in particular, how to use decomposable graphical models for efficient inference in covariance selection and multivariate regression problems. The first aim of the thesis is to show that decomposable graphical models are worth using within a Bayesian framework. The second aim is to make the techniques of graphical models fully accessible to statisticians. To achieve these aims the thesis makes a number of statistical contributions. First, it proposes a new prior for decomposable graphs and a simulation methodology for estimating this prior. Second, it proposes a number of Markov chain Monte Carlo sampling schemes based on graphical techniques. The thesis also presents some new graphical results, and some existing results are reproved to make them more readily understood. Appendix 8.1 contains all the programs written to carry out the inference discussed in the thesis, together with both a summary of the theory on which they are based and a line by line description of how each routine works.
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Dibble, Emily. "The interpretation of graphs and tables /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/9101.

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Hossain, Mahmud Shahriar. "Apriori approach to graph-based clustering of text documents." Thesis, Montana State University, 2008. http://etd.lib.montana.edu/etd/2008/hossain/HossainM0508.pdf.

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This thesis report introduces a new technique of document clustering based on frequent senses. The developed system, named GDClust (Graph-Based Document Clustering) [1], works with frequent senses rather than dealing with frequent keywords used in traditional text mining techniques. GDClust presents text documents as hierarchical document-graphs and uses an Apriori paradigm to find the frequent subgraphs, which reflect frequent senses. Discovered frequent subgraphs are then utilized to generate accurate sense-based document clusters. We propose a novel multilevel Gaussian minimum support strategy for candidate subgraph generation. Additionally, we introduce another novel mechanism called Subgraph-Extension mining that reduces the number of candidates and overhead imposed by the traditional Apriori-based candidate generation mechanism. GDClust utilizes an English language thesaurus (WordNet [2]) to construct document-graphs and exploits graph-based data mining techniques for sense discovery and clustering. It is an automated system and requires minimal human interaction for the clustering purpose.
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Coetzer, Audrey. "Criticality of the lower domination parameters of graphs." Thesis, Link to the online version, 2007. http://hdl.handle.net/10019/1051.

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Books on the topic "Prediction theory Graphic methods"

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Scheinerman, Edward R. Fractional graph theory: A rational approach to the theory of graphs. Minola, N.Y: Dover Publications, 2011.

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Biggs, Norman. Graph theory, 1736-1936. Oxford [Oxfordshire]: Clarendon Press, 1986.

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Graph algebras and automata. New York: Marcel Dekker, 2003.

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Graph theory and its engineering applications. Singapore: World Scientific, 1997.

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Graphs. 2nd ed. Amsterdam: North Holland, 1985.

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International Conference on Computational Methods for Predicting Material Processing Defects (1987 Cachan, France). Computational methods for predicting material processing defects: Proceedings of the International Conference on Computational Methods for Predicting Material Processing Defects, September 8-11, 1987, Cachan, France. Amsterdam: Elsevier, 1987.

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1957-, Zhang Ping, ed. Introduction to graph theory. Boston: McGraw-Hill Higher Education, 2005.

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Large-scale inference: Empirical Bayes methods for estimation, testing, and prediction. Cambridge: Cambridge University Press, 2010.

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Chen, Wai-Kai. Graph theory and its engineering applications. Singapore: World Scientific, 1997.

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Hodges, Wilfrid. Building models by games. Mineola, N.Y: Dover Publications, 2006.

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Book chapters on the topic "Prediction theory Graphic methods"

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Ramanathan, Jayakumar. "Prediction Theory." In Methods of Applied Fourier Analysis, 63–86. Boston, MA: Birkhäuser Boston, 1998. http://dx.doi.org/10.1007/978-1-4612-1756-5_3.

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Gonzalez, Wenceslao J. "Methods of Prediction in Economics." In Theory and Decision Library A:, 251–84. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-08885-3_10.

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Zeiler, P., F. Müller, and B. Bertsche. "New methods for the availability prediction with confidence level." In Risk, Reliability and Safety: Innovating Theory and Practice, 313–20. Taylor & Francis Group, 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742: CRC Press, 2016. http://dx.doi.org/10.1201/9781315374987-50.

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Ma, Huan, and Wei Wang. "A Label Propagation Based User Locations Prediction Algorithm in Social Network." In Communications in Computer and Information Science, 165–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-33-4922-3_12.

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AbstractNetwork community detection is an important service provided by social networks, and social network user location can greatly improve the quality of community detection. Label propagation is one of the main methods to realize the user location prediction. The traditional label propagation algorithm has the problems including “location label countercurrent” and the update randomness of node location label, which seriously affects the accuracy of user location prediction. In this paper, a new location prediction algorithm for social networks based on improved label propagation algorithm is proposed. By computing the K-hop public neighbor of any two point in the social network graph, the nodes with the maximal similarity and their K-hopping neighbors are merged to constitute the initial label propagation set. The degree of nodes not in the initial set are calculated. The node location labels are updated asynchronously is adopted during the iterative process, and the node with the largest degree is selected to update the location label. The improvement proposed solves the “location label countercurrent” and reduces location label updating randomness. The experimental results show that the proposed algorithm improves the accuracy of position prediction and reduces the time cost compared with the traditional algorithms.
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Middendorp, Peter, Marcel W. Bielefeld, and Joost Bakker. "Rapid Load Testing Prediction Models." In 10th International Conference on Stress Wave Theory and Testing Methods for Deep Foundations, 286–307. 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959: ASTM International, 2019. http://dx.doi.org/10.1520/stp161120170237.

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Obodan, Natalia I., Victor J. Adlucky, and Vasilii A. Gromov. "Prediction and Control of Buckling: The Inverse Bifurcation Problems for von Karman Equations." In Applied Mathematical Analysis: Theory, Methods, and Applications, 353–81. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-99918-0_11.

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Shapiro, Richard A. "Prediction of Dispersive Errors in Numerical Solution of the Euler Equations." In Nonlinear Hyperbolic Equations — Theory, Computation Methods, and Applications, 552–61. Wiesbaden: Vieweg+Teubner Verlag, 1989. http://dx.doi.org/10.1007/978-3-322-87869-4_54.

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Sommerfeld, Martin. "Numerical Prediction of Shock Wave Focusing Phenomena in Air with Experimental Verification." In Nonlinear Hyperbolic Equations — Theory, Computation Methods, and Applications, 562–73. Wiesbaden: Vieweg+Teubner Verlag, 1989. http://dx.doi.org/10.1007/978-3-322-87869-4_55.

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Pulido, Martha, and Patricia Melin. "Ensemble Recurrent Neural Networks for Complex Time Series Prediction with Integration Methods." In Fuzzy Logic Hybrid Extensions of Neural and Optimization Algorithms: Theory and Applications, 71–83. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68776-2_4.

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Ceder, G., P. D. Tepesch, G. D. Garbulsky, and A. F. Kohan. "Lattice Models and Cluster Expansions for the Prediction of Oxide Phase Diagrams and Defect Arrangements." In Theory and Applications of the Cluster Variation and Path Probability Methods, 187–201. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4613-0419-7_12.

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Conference papers on the topic "Prediction theory Graphic methods"

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Islam, Md Kamrul, Sabeur Aridhi, and Malika Smail-Tabbone. "Appraisal Study of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs." In 2nd International Conference on Machine Learning &Trends (MLT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.111106.

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The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many real-world graphs though they are heuristic. On the other hand, graph embedding approaches learn low-dimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This appraisal paper studies a selection of methods from both categories on several benchmark (homogeneous) graphs with different properties from various domains. Beyond the intra and inter category comparison of the performances of the methods our aim is also to uncover interesting connections between Graph Neural Network(GNN)-based methods and heuristic ones as a means to alleviate the black-box well-known limitation.
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Moon, Yong-Mo, and Sridhar Kota. "Synthesis of Reconfigurable Machine Tools With Screw Theory." In ASME 2000 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2000. http://dx.doi.org/10.1115/detc2000/mech-14066.

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Abstract In this paper, we present a systematic methodology for designing Reconfigurable Machine Tools (RMTs). The synthesis methodology takes as input a set of functional requirements — a set of process plans and outputs a set of kinematically-viable reconfigurable machine tools that meet the given design specifications. We present a mathematical framework for synthesis of machine tools using a library of building blocks. The framework is rooted in (a) graph theoretic methods of enumeration of alternate structural configurations and (b) screw theory that enables us to manipulate matrix representations of motions to identify appropriate kinematic building blocks. The methodology described in this paper provides a mathematical framework to address dynamic stiffness, and accuracy prediction of the kinematically-viable designs that are generated by the synthesis procedure. This methodology has been implemented in a program, called PREMADE (PRogram for REconfigurable MAchine tool DEsign), and the results are validated against commercial machine tool designs.
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Jiang, P., I. Bychkov, J. Liu, T. Li, and A. Hmelnov. "Traffic flow prediction for vehicle emission calculation based on graph convolutional networks." In 1st International Workshop on Advanced Information and Computation Technologies and Systems 2020. Crossref, 2021. http://dx.doi.org/10.47350/aicts.2020.10.

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Monitoring the distribution of vehicle exhaust emissions within the city is a very challenging problem since it is affected by many complex factors, such as spatial-temporal correlation and the other environment conditions. In addition, the technology of using sensors to directly monitor vehicle exhaust emissions is still in the initial stage, and it is hard to implement direct monitoring in a large area. Thus, we use the existing environmental theory to measure the distribution of vehicle exhaust emissions in cities by traffic volume. In this paper, the problem we need to solve is how to use the data of sparse monitoring stations and inherent traffic network to infer the spatial-temporal distribution of traffic volume. In order to solve this problem, we propose a graph convolutional network model to extract the characteristics of traffic data and other features. We have done a lot of experiments on real traffic data sets. The experimental results show that the proposed method performs better than the existing methods.
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Namouz, Essam Z., and Joshua D. Summers. "Comparison of Graph Generation Methods for Structural Complexity Based Assembly Time Estimation." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12300.

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This paper compares two different methods of graph generation for input into the complexity connectivity method to estimate the assembly time of a product. The complexity connectivity method builds predictive models for assembly time based on twenty-nine complexity metrics applied to the product graphs. Previously the part connection graph was manually created, but recently the Assembly Mate Method and the Interference Detection Method have introduced new automated tools for creating the part connectivity graphs. These graph generation methods are compared on their ability to predict the assembly time of multiple products. For this research, eleven consumers products are used to train an artificial neural network and three products are reserved for testing. The results indicate that both the Assembly Mate Method and the Interference Detection Method can create connectivity graphs that predict the assembly time of a product to within 45% of the target time. The Interference Detection Method showed less variability than the Assembly Mate Method in the time estimations. The Assembly Mate Method is limited to only SolidWorks assembly files, while the Interference Detection Method is more flexible and can operate on different file formats including IGES, STEP, and Parasolid. Overall, both of the graph generation methods provide a suitable automated tool to form the connectivity graph, but the Interference Detection Method provides less variance in predicting the assembly time and is more flexible in terms of file types that can be used.
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Xu, Nuo, Pinghui Wang, Long Chen, Jing Tao, and Junzhou Zhao. "MR-GNN: Multi-Resolution and Dual Graph Neural Network for Predicting Structured Entity Interactions." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/551.

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Predicting interactions between structured entities lies at the core of numerous tasks such as drug regimen and new material design. In recent years, graph neural networks have become attractive. They represent structured entities as graphs, and then extract features from each individual graph using graph convolution operations. However, these methods have some limitations: i) their networks only extract features from a fix-sized subgraph structure (i.e., a fix-sized receptive field) of each node, and ignore features in substructures of different sizes, and ii) features are extracted by considering each entity independently, which may not effectively reflect the interaction between two entities. To resolve these problems, we present {\em MR-GNN}, an end-to-end graph neural network with the following features: i) it uses a multi-resolution based architecture to extract node features from different neighborhoods of each node, and, ii) it uses dual graph-state long short-term memory networks (LSTMs) to summarize local features of each graph and extracts the interaction features between pairwise graphs. Experiments conducted on real-world datasets show that MR-GNN improves the prediction of state-of-the-art methods.
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Garg, Sankalp, Navodita Sharma, Woojeong Jin, and Xiang Ren. "Temporal Attribute Prediction via Joint Modeling of Multi-Relational Structure Evolution." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/386.

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Time series prediction is an important problem in machine learning. Previous methods for time series prediction did not involve additional information. With a lot of dynamic knowledge graphs available, we can use this additional information to predict the time series better. Recently, there has been a focus on the application of deep representation learning on dynamic graphs. These methods predict the structure of the graph by reasoning over the interactions in the graph at previous time steps. In this paper, we propose a new framework to incorporate the information from dynamic knowledge graphs for time series prediction. We show that if the information contained in the graph and the time series data are closely related, then this inter-dependence can be used to predict the time series with improved accuracy. Our framework, DArtNet, learns a static embedding for every node in the graph as well as a dynamic embedding which is dependent on the dynamic attribute value (time-series). Then it captures the information from the neighborhood by taking a relation specific mean and encodes the history information using RNN. We jointly train the model link prediction and attribute prediction. We evaluate our method on five specially curated datasets for this problem and show a consistent improvement in time series prediction results. We release the data and code of model DArtNet for future research.
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Fatemi, Bahare, Perouz Taslakian, David Vazquez, and David Poole. "Knowledge Hypergraphs: Prediction Beyond Binary Relations." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/303.

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Knowledge graphs store facts using relations between two entities. In this work, we address the question of link prediction in knowledge hypergraphs where relations are defined on any number of entities. While techniques exist (such as reification) that convert non-binary relations into binary ones, we show that current embedding-based methods for knowledge graph completion do not work well out of the box for knowledge graphs obtained through these techniques. To overcome this, we introduce HSimplE and HypE, two embedding-based methods that work directly with knowledge hypergraphs. In both models, the prediction is a function of the relation embedding, the entity embeddings and their corresponding positions in the relation. We also develop public datasets, benchmarks and baselines for hypergraph prediction and show experimentally that the proposed models are more effective than the baselines.
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Шарнин, Михаил, Michael Charnine, Евгений Соколов, Evgeniy Sokolov, Алексей Клоков, and Aleksey Klokov. "Semantic Approach to Visualization of Evolution Dynamics of Topic Trends in Space of Scientific Publications Using t-SNE and Web-based 3D Graphics." In 29th International Conference on Computer Graphics, Image Processing and Computer Vision, Visualization Systems and the Virtual Environment GraphiCon'2019. Bryansk State Technical University, 2019. http://dx.doi.org/10.30987/graphicon-2019-1-48-52.

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This paper describes a semantic approach to visualization of 3D cyberspace of Artificial Intelligence (AI) publications and their topic trends evolution using web-based 3D graphics. The purpose of research is to group AI publications with same subject into clusters for further visualization of topic trends dynamics. An unsupervised method and algorithm for visualizing the dynamics of topic trends by generating a time series of 2D and 3D semantic visual maps with predictive information is described. The method includes semantic similarity measure and citation prediction for documents, topic modeling and clustering, dimensionality reduction, virtual reality technology, representation of dynamics using time filters. As an example of particular implementation, the method is demonstrated on AI collection data using technologies of neural network prediction, LDA clustering, t-SNE dimensionality reduction, WebVR visualization. Cluster dynamics associated with scientific trends is analyzed. The growth in number of clusters and their consolidation during the period from 1954 to 1993 is demonstrated. It is shown that 3D visual map better preserves articles similarity and highdimensional clusters structure than 2D visual map. The proposed cyberspace implemented by WebVR and interactive 3D graphics can be considered as a dynamic learning environment that is convenient for discovering new significant articles, ideas and trends.
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Li, Zhongyang, Xiao Ding, and Ting Liu. "Constructing Narrative Event Evolutionary Graph for Script Event Prediction." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/584.

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Script event prediction requires a model to predict the subsequent event given an existing event context. Previous models based on event pairs or event chains cannot make full use of dense event connections, which may limit their capability of event prediction. To remedy this, we propose constructing an event graph to better utilize the event network information for script event prediction. In particular, we first extract narrative event chains from large quantities of news corpus, and then construct a narrative event evolutionary graph (NEEG) based on the extracted chains. NEEG can be seen as a knowledge base that describes event evolutionary principles and patterns. To solve the inference problem on NEEG, we present a scaled graph neural network (SGNN) to model event interactions and learn better event representations. Instead of computing the representations on the whole graph, SGNN processes only the concerned nodes each time, which makes our model feasible to large-scale graphs. By comparing the similarity between input context event representations and candidate event representations, we can choose the most reasonable subsequent event. Experimental results on widely used New York Times corpus demonstrate that our model significantly outperforms state-of-the-art baseline methods, by using standard multiple choice narrative cloze evaluation.
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Pan, Shirui, Ruiqi Hu, Guodong Long, Jing Jiang, Lina Yao, and Chengqi Zhang. "Adversarially Regularized Graph Autoencoder for Graph Embedding." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/362.

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Graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics. Most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data, but they have mostly ignored the data distribution of the latent codes from the graphs, which often results in inferior embedding in real-world graph data. In this paper, we propose a novel adversarial graph embedding framework for graph data. The framework encodes the topological structure and node content in a graph to a compact representation, on which a decoder is trained to reconstruct the graph structure. Furthermore, the latent representation is enforced to match a prior distribution via an adversarial training scheme. To learn a robust embedding, two variants of adversarial approaches, adversarially regularized graph autoencoder (ARGA) and adversarially regularized variational graph autoencoder (ARVGA), are developed. Experimental studies on real-world graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction, graph clustering, and graph visualization tasks.
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