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Relevant bibliographies by topics / Graphs; Non-negative

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Academic literature on the topic 'Graphs; Non-negative'

Author: Grafiati

Published: 4 June 2021

Last updated: 7 February 2022

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Journal articles on the topic "Graphs; Non-negative"

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Teng, Wenshun, and Huijuan Wang. "Vertex arboricity of graphs embedded in a surface of non-negative Euler characteristic." Discrete Mathematics, Algorithms and Applications 12, no. 06 (July 30, 2020): 2050080. http://dx.doi.org/10.1142/s1793830920500809.

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The vertex arboricity [Formula: see text] of a graph [Formula: see text] is the minimum number of colors the vertices of the graph [Formula: see text] can be colored so that every color class induces an acyclic subgraph of [Formula: see text]. There are many results on the vertex arboricity of planar graphs. In this paper, we replace planar graphs with graphs which can be embedded in a surface [Formula: see text] of Euler characteristic [Formula: see text]. We prove that for the graph [Formula: see text] which can be embedded in a surface [Formula: see text] of Euler characteristic [Formula: see text] if no [Formula: see text]-cycle intersects a [Formula: see text]-cycle, or no [Formula: see text]-cycle intersects a [Formula: see text]-cycle, then [Formula: see text] in addition to the [Formula: see text]-regular quadrilateral mesh.

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YANHAONA, MUHAMMAD NUR, MD SHAMSUZZOHA BAYZID, and MD SAIDUR RAHMAN. "DISCOVERING PAIRWISE COMPATIBILITY GRAPHS." Discrete Mathematics, Algorithms and Applications 02, no. 04 (December 2010): 607–23. http://dx.doi.org/10.1142/s1793830910000917.

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Let T be an edge weighted tree, let dT(u, v) be the sum of the weights of the edges on the path from u to v in T, and let d min and d max be two non-negative real numbers such that d min ≤ d max . Then a pairwise compatibility graph of T for d min and d max is a graph G = (V, E), where each vertex u' ∈ V corresponds to a leaf u of T and there is an edge (u', v') ∈ E if and only if d min ≤ dT(u, v) ≤ d max . A graph G is called a pairwise compatibility graph (PCG) if there exists an edge weighted tree T and two non-negative real numbers d min and d max such that G is a pairwise compatibility graph of T for d min and d max . Kearney et al. conjectured that every graph is a PCG [3]. In this paper, we refute the conjecture by showing that not all graphs are PCG s . Moreover, we recognize several classes of graphs as pairwise compatibility graphs. We identify two restricted classes of bipartite graphs as PCG. We also show that the well known tree power graphs and some of their extensions are PCGs.

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Jiménez González, Jesús Arturo. "Incidence graphs and non-negative integral quadratic forms." Journal of Algebra 513 (November 2018): 208–45. http://dx.doi.org/10.1016/j.jalgebra.2018.07.020.

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Derikvand, Tajedin, and Mohammad Reza Oboudi. "Small graphs with exactly two non-negative eigenvalues." Algebraic structures and their applications 4, no. 1 (August 1, 2017): 1–18. http://dx.doi.org/10.29252/asta.4.1.1.

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Zhang, Kewei. "On non-negative quasiconvex functions with unbounded zero sets." Proceedings of the Royal Society of Edinburgh: Section A Mathematics 127, no. 2 (1997): 411–22. http://dx.doi.org/10.1017/s0308210500023726.

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We construct nontrivial, non-negative quasiconvex functions denned on M2×2 with p-th order growth such that the zero sets of the functions are Lipschitz graphs of mappings from subsets of a fixed two-dimensional subspace to its orthogonal complement. We assume that the graphs do not have rank-one connections with the Lipschitz constants sufficiently small. In particular, we are able to construct quasiconvex functions which are homogeneous of degree p (p > 1) and ‘conjugating’ invariant.

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Oboudi, Mohammad Reza. "Characterization of graphs with exactly two non-negative eigenvalues." Ars Mathematica Contemporanea 12, no. 2 (December 23, 2016): 271–86. http://dx.doi.org/10.26493/1855-3974.1077.5b6.

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Koledin, Tamara, and Zoran Stanić. "Regular bipartite graphs with three distinct non-negative eigenvalues." Linear Algebra and its Applications 438, no. 8 (April 2013): 3336–49. http://dx.doi.org/10.1016/j.laa.2012.12.036.

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Chung, Fan, Yong Lin, and S. T. Yau. "Harnack inequalities for graphs with non-negative Ricci curvature." Journal of Mathematical Analysis and Applications 415, no. 1 (July 2014): 25–32. http://dx.doi.org/10.1016/j.jmaa.2014.01.044.

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Alomari, Omar, Mohammad Abudayah, and Torsten Sander. "The non-negative spectrum of a digraph." Open Mathematics 18, no. 1 (February 19, 2020): 22–35. http://dx.doi.org/10.1515/math-2020-0005.

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Abstract Given the adjacency matrix A of a digraph, the eigenvalues of the matrix AAT constitute the so-called non-negative spectrum of this digraph. We investigate the relation between the structure of digraphs and their non-negative spectra and associated eigenvectors. In particular, it turns out that the non-negative spectrum of a digraph can be derived from the traditional (adjacency) spectrum of certain undirected bipartite graphs.

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B. Boomadevi, V. Gopal, and B. Boomadevi. "ON SIGNED (NON-NEGATIVE) MAJORITY TOTAL DOMINATION OF SOME GRAPHS." Advances in Mathematics: Scientific Journal 9, no. 4 (July 3, 2020): 2039–45. http://dx.doi.org/10.37418/amsj.9.4.62.

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Dissertations / Theses on the topic "Graphs; Non-negative"

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Gerke, Stefanie. "Weighted colouring and channel assignment." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325977.

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Hsieh, Yu-Cheng, and 謝侑澄. "Financial Distress Data Mining by Graph Regularized Non-negative Matrix Factorization." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/65634440383243948902.

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碩士
國立彰化師範大學
企業管理學系
101
In recent decades, due to the dramatic changes in the global economic, bankruptcy prediction become an important issue for investors and government because the enterprise bankruptcy would incur large losses for investors and increase social costs. Thus, many researches study how to predict whether the company would suffer financial crisis or not. Most of the early warning models were based on financial ratios (i.e. Altman, 1968; Martin, 1977). However, many literatures show that the factors of bankruptcy are not only financial ratios, there are many factors would impact the predict of financial crisis, such as corporate governance, macroeconomic, Audit Opinions, Auditor Changes and audit firm changes. As the reasons, we consider these factors to build the financial distress prediction models. The data are sampled from Taiwan Stock Exchange Corporation (TWSE) from 1999 to 2010, including 111 variables. However, high-dimensional data not only decrease compute speed but also incur curse of dimensionality. For solve this problem, we use Nonnegative Matrix Factorization (NMF) and Graph Regularized Non-negative Matrix Factorization (GNMF) to reduce dimensions, and construct financial distress prediction models by logistic regression, neural network (NN), support vector machine (SVM) and ensemble algorithms-bagging.

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Book chapters on the topic "Graphs; Non-negative"

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Suri, N. N. R. Ranga, Musti Narasimha Murty, and Gopalasamy Athithan. "Mining Anomalous Sub-graphs in Graph Data Using Non-negative Matrix Factorization." In Lecture Notes in Computer Science, 88–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45062-4_11.

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Mitra, Anasua, Priyesh Vijayan, Srinivasan Parthasarathy, and Balaraman Ravindran. "A Unified Non-Negative Matrix Factorization Framework for Semi Supervised Learning on Graphs." In Proceedings of the 2020 SIAM International Conference on Data Mining, 487–95. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.55.

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Chen, Pan, Yangcheng He, Hongtao Lu, and Li Wu. "Constrained Non-negative Matrix Factorization with Graph Laplacian." In Neural Information Processing, 635–44. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26555-1_72.

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Ghanbari, Yasser, John Herrington, Ruben C. Gur, Robert T. Schultz, and Ragini Verma. "Locality Preserving Non-negative Basis Learning with Graph Embedding." In Lecture Notes in Computer Science, 316–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38868-2_27.

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Dai, Xiangguang, Keke Zhang, Juntang Li, Jiang Xiong, and Nian Zhang. "Robust Graph Regularized Non-negative Matrix Factorization for Image Clustering." In Advances in Neural Networks – ISNN 2020, 244–50. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-64221-1_21.

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Ogino, Hiroki, and Tetsuya Yoshida. "Topic Graph Based Non-negative Matrix Factorization for Transfer Learning." In Lecture Notes in Computer Science, 260–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21916-0_29.

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Hao, Yang, Congying Han, Guangqi Shao, and Tiande Guo. "Generalized Graph Regularized Non-negative Matrix Factorization for Data Representation." In Lecture Notes in Electrical Engineering, 1–12. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34528-9_1.

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Long, Xianzhong, Jian Xiong, and Yun Li. "Graph Learning Regularized Non-negative Matrix Factorization for Image Clustering." In Communications in Computer and Information Science, 351–60. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63823-8_41.

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Yang, Caifeng, Tao Liu, Guifu Lu, Zhenxin Wang, and Zhi Deng. "Improved Non-negative Matrix Factorization Algorithm for Sparse Graph Regularization." In Communications in Computer and Information Science, 221–32. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5940-9_17.

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Du, Haishun, Qingpu Hu, Xudong Zhang, and Yandong Hou. "Image Feature Extraction via Graph Embedding Regularized Projective Non-negative Matrix Factorization." In Communications in Computer and Information Science, 196–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-45646-0_20.

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Conference papers on the topic "Graphs; Non-negative"

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Marczak, Grzegorz, Daniel Simson, and Katarzyna Zajac. "On Computing Non-negative Loop-Free Edge-Bipartite Graphs." In 2013 15th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2013. http://dx.doi.org/10.1109/synasc.2013.16.

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Hanwang Zhang, Zheng-Jun Zha, Shuicheng Yan, Meng Wang, and Tat-Seng Chua. "Robust Non-negative Graph Embedding: Towards noisy data, unreliable graphs, and noisy labels." In 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2012. http://dx.doi.org/10.1109/cvpr.2012.6247961.

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Qureshi, Muhammad Aasim, Mohd Fadzil Hassan, Sohail Safdar, and Rehan Akbar. "Two Phase Shortest Path Algorithm for Non-negative Weighted Undirected Graphs." In 2010 Second International Conference on Communication Software and Networks. IEEE, 2010. http://dx.doi.org/10.1109/iccsn.2010.97.

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Santos, Tanilson D., Jayme Szwarcfiter, Uéverton S. Souza, and Claudson F. Bornstein. "On the Helly Property of Some Intersection Graphs." In Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/ctd.2021.15752.

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An EPG graph G is an edge-intersection graph of paths on a grid. In this thesis, we analyze structural characterizations and complexity aspects regarding EPG graphs. Our main focus is on the class of B1-EPG graphs whose intersection model satisfies well-known the Helly property, called Helly-B1-EPG. We show that the problem of recognizing Helly-B1-EPG graphs is NP-complete. Besides, other intersection graph classes such as VPG, EPT, and VPT were also studied. We completely solve the problem of determining the Helly and strong Helly numbers of Bk-EPG graphs and Bk-VPG graphs for each non-negative integer k. Finally, we show that every Chordal B1-EPG graph is at the intersection of VPT and EPT.

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Qureshi, M. Aasim, Mohd Fadzil Hassan, Sohail Safdar, Rehan Akbar, and Rabia Sammi. "An edge-wise linear shortest path algorithm for non negative weighted undirected graphs." In the 6th International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1838002.1838079.

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Chan, Jeffrey, Wei Liu, Andrey Kan, Christopher Leckie, James Bailey, and Kotagiri Ramamohanarao. "Discovering latent blockmodels in sparse and noisy graphs using non-negative matrix factorisation." In the 22nd ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2505515.2505595.

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Muhammad Aasim Qureshi, Mohd Fadzil Hassan, Sohail Safdar, Rehan Akbar, and Rabia Sammi. "Shortest path algorithm with pre-calculated single link failure recovery for non-negative weighted undirected graphs." In 2010 International Conference on Information and Emerging Technologies (ICIET). IEEE, 2010. http://dx.doi.org/10.1109/iciet.2010.5625724.

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Mukherjee, Arpan, Rahul Rai, Puneet Singla, Tarunraj Singh, and Abani Patra. "Non-Negative Matrix Factorization Based Uncertainty Quantification Method for Complex Networked Systems." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46087.

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The behavior of large networked systems with underlying complex nonlinear dynamic are hard to predict. With increasing number of states, the problem becomes even harder. Quantifying uncertainty in such systems by conventional methods requires high computational time and the accuracy obtained in estimating the state variables can also be low. This paper presents a novel computational Uncertainty Quantifying (UQ) method for complex networked systems. Our approach is to represent the complex systems as networks (graphs) whose nodes represent the dynamical units, and whose links stand for the interactions between them. First, we apply Non-negative Matrix Factorization (NMF) based decomposition method to partition the domain of the dynamical system into clusters, such that the inter-cluster interaction is minimized and the intra-cluster interaction is maximized. The decomposition method takes into account the dynamics of individual nodes to perform system decomposition. Initial validation results on two well-known dynamical systems have been performed. The validation results show that uncertainty propagation error quantified by RMS errors obtained through our algorithms are competitive or often better, compared to existing methods.

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Jianchao Yang, Shuicheng Yang, Yun Fu, Xuelong Li, and Thomas Huang. "Non-negative graph embedding." In 2008 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2008. http://dx.doi.org/10.1109/cvpr.2008.4587665.

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Maly, Jan, Miroslaw Truszczynski, and Stefan Woltran. "Preference Orders on Families of Sets - When Can Impossibility Results Be Avoided?" 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.2018/60.

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Lifting a preference order on elements of some universe to a preference order on subsets of this universe is often guided by postulated properties the lifted order should have. Well-known impossibility results pose severe limits on when such liftings exist if all non-empty subsets of the universe are to be ordered. The extent to which these negative results carry over to other families of sets is not known. In this paper, we consider families of sets that induce connected subgraphs in graphs. For such families, common in applications, we study whether lifted orders satisfying the well-studied axioms of dominance and (strict) independence exist for every or, in another setting, only for some underlying order on elements (strong and weak orderability). We characterize families that are strongly and weakly orderable under dominance and strict independence, and obtain a tight bound on the class of families that are strongly orderable under dominance and independence.

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Reports on the topic "Graphs; Non-negative"

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Exploring the Prospects of Using 3D Printing Technology in the South African Human Settlements. Academy of Science of South Africa (ASSAf), 2021. http://dx.doi.org/10.17159/assaf.2021/0074.

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South Africa is a country with significant socio-economic development challenges, with the majority of South Africans having limited or non-existent access to basic infrastructure, services, housing and socio-economic opportunities etc. The urban housing backlog currently exceeds 2.4 million houses, with many families living in informal settlements. The Breaking New Grounds Policy, 2014 for the creation of sustainable human settlements, acknowledges the challenges facing human settlements, such as, decreasing human settlements grants allocation, increasing housing backlog, mushrooming of informal settlements and urbanisation. The White Paper on Science, Technology and Innovation (STI), 2019 notes that South Africa has not yet fully benefited from the potential of STI in addressing the socio-economic challenges and seeks to support the circular economy principles which entail a systematic change of moving to a zero or low waste resource-efficient society. Further to this, the Science and Technology Roadmap’s intention is to unlock the potential of South Africa’s human settlements for a decent standard of living through the smart uptake of science, technology and innovation. One such novel technology is the Three-Dimensional (3D) printing technology, which has produced numerous incredible structures around the world. 3D printing is a computer-controlled industrial manufacturing process which encompasses additive means of production to create 3D shapes. The effects of such a technology have a potential to change the world we live in and could subsequently pave the roadmap to improve on housing delivery and reduce the negative effects of conventional construction methods on the environment. To this end, the Academy of Science of South Africa (ASSAf), in partnership with the Department of Science and Innovation (DSI) and the University of Johannesburg (UJ) hosted the second virtual IID seminar titled: Exploring the Prospects of Using 3D Printing Technology in the South African Human Settlements, on 01 March 2021 to explore the potential use of 3D printing technology in human settlements. The webinar presented preliminary findings from a study conducted by UJ, addressing the following topics: 1. The viability of 3D printing technology 2. Cost comparison of 3D printed house to conventional construction 3. Preliminary perceptions on 3D printing of houses Speakers included: Dr Jennifer Mirembe (NDoHS), Dr Jeffrey Mahachi, Mr Refilwe Lediga, Mr Khululekani Ntakana and Dr Luxien Ariyan, all from UJ. There was a unanimous consensus that collaborative efforts from all stakeholders are key to take advantage of this niche technology. @ASSAf_Official; @dsigovza; @go2uj; @The_DHS; #SA 3D_Printing; #3D Print_Housing; #IID

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