Academic literature on the topic 'Random graph generation'
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Journal articles on the topic "Random graph generation"
Vizi, Máté Benjámin, Péter Árpád Mizsák, and Tamás Kalmár-Nagy. "Saturation in Regular, Exotic and Random Pore Networks." Fluctuation and Noise Letters 17, no. 03 (September 2018): 1850024. http://dx.doi.org/10.1142/s0219477518500244.
Full textWagaman, Amy. "Graph construction and random graph generation for modeling protein structures." Statistical Analysis and Data Mining 6, no. 6 (August 26, 2013): 482–95. http://dx.doi.org/10.1002/sam.11203.
Full textSun, Wenbo, and Ivona Bezáková. "Sampling Random Chordal Graphs by MCMC (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13929–30. http://dx.doi.org/10.1609/aaai.v34i10.7237.
Full textAbidin, Zainal, and Agus Zainal Arifin. "Membatasi k-Ketenggaan Simpul dalam Pembangkitan Random Graph Metode Erdos Royi untuk Meningkatkan Kinerja Komputasi." CAUCHY 1, no. 2 (May 15, 2010): 97. http://dx.doi.org/10.18860/ca.v1i2.1706.
Full textAllen, Thomas E., Judy Goldsmith, Hayden Elizabeth Justice, Nicholas Mattei, and Kayla Raines. "Uniform Random Generation and Dominance Testing for CP-Nets." Journal of Artificial Intelligence Research 59 (August 29, 2017): 771–813. http://dx.doi.org/10.1613/jair.5455.
Full textBasuli, Krishnendu, and Saptershi Naskar. "Generation of a random simple graph and its graphical presentation." Ubiquity 2008, March (March 2008): 1–4. http://dx.doi.org/10.1145/1366313.1361266.
Full textBOROVIK, ALEXANDRE V., EVGENII I. KHUKHRO, and ALEXEI G. MYASNIKOV. "THE ANDREWS–CURTIS CONJECTURE AND BLACK BOX GROUPS." International Journal of Algebra and Computation 13, no. 04 (August 2003): 415–36. http://dx.doi.org/10.1142/s0218196703001468.
Full textMorris, James F., Jerome W. O’Neal, and Richard F. Deckro. "A random graph generation algorithm for the analysis of social networks." Journal of Defense Modeling and Simulation: Applications, Methodology, Technology 11, no. 3 (June 11, 2013): 265–76. http://dx.doi.org/10.1177/1548512912450370.
Full textDaskin, Anmer, Ananth Grama, and Sabre Kais. "Quantum random state generation with predefined entanglement constraint." International Journal of Quantum Information 12, no. 05 (August 2014): 1450030. http://dx.doi.org/10.1142/s0219749914500300.
Full textBressan, Stéphane, Alfredo Cuzzocrea, Panagiotis Karras, Xuesong Lu, and Sadegh Heyrani Nobari. "An effective and efficient parallel approach for random graph generation over GPUs." Journal of Parallel and Distributed Computing 73, no. 3 (March 2013): 303–16. http://dx.doi.org/10.1016/j.jpdc.2012.09.010.
Full textDissertations / Theses on the topic "Random graph generation"
Parikh, Nidhi Kiranbhai. "Generating Random Graphs with Tunable Clustering Coefficient." Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/31591.
Full textMaster of Science
Perarnau, Swann. "Environnements pour l'analyse expérimentale d'applications de calcul haute performance." Phd thesis, Université de Grenoble, 2011. http://tel.archives-ouvertes.fr/tel-00650047.
Full textPenschuck, Manuel [Verfasser], Ulrich [Akademischer Betreuer] Meyer, Ulrich [Gutachter] Meyer, Peter [Gutachter] Sanders, and Georg [Gutachter] Schnitger. "Scalable generation of random graphs / Manuel Penschuck ; Gutachter: Ulrich Meyer, Peter Sanders, Georg Schnitger ; Betreuer: Ulrich Meyer." Frankfurt am Main : Universitätsbibliothek Johann Christian Senckenberg, 2021. http://d-nb.info/1239730012/34.
Full textBhuiyan, Md Hasanuzzaman. "Parallel Algorithms for Switching Edges and Generating Random Graphs from Given Degree Sequences using HPC Platforms." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/80299.
Full textPh. D.
Duvignau, Romaric. "Maintenance et simulation de graphes aléatoires dynamiques." Thesis, Bordeaux, 2015. http://www.theses.fr/2015BORD0177/document.
Full textWe study the problem of maintaining a given distribution of randomgraphs under an arbitrary sequence of vertex insertions and deletions. Keeping inmind our objective to model the evolution of dynamic logical networks, we work ina local model where we do not have direct access to the list of all vertices. Instead,we assume access to a global primitive that returns a random vertex, chosen uniformlyfrom the whole vertex set. The maintenance problem has been explored onseveral simple random graph models (Erdos–Rényi random graphs, pairing modelbased random graphs, uniform k-out graphs). For each model, one or several updatealgorithms for the maintenance task have been described and analyzed ; the mostelaborate of them are asymptically optimal. The maintenance task rise several simulationissues linked to our distributed context. In particular, we have focused onmaintenability of random graph distributions and simulability of families of probabilitydistributions over integers in our local random model. Special attention hasbeen paid to efficient simulation of particular distributions we were interested in(certain binomial distributions). The latter has been obtained through the use ofproperties of a new generation tree for permutations, which has been introducedalong the way
Lesparre, Youen. "Evaluation de l'affectation des tâches sur une architecture à mémoire distribuée pour des modèles flot de données." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066086/document.
Full textWith the increasing use of smart-phones, connected objects or automated vehicles, embedded systems have become ubiquitous in our living environment. These systems are often highly constrained in terms of power consumption and size. They are more and more implemented with many-core processor array that allow, rapid design to meet stringent real-time constraints while operating at relatively low frequency, with reduced power consumption.Running an application on a processor array requires dispatching its tasks on the processors in order to meet capacity and performance constraints. This mapping problem is known to be NP-complete.The contributions of this thesis are threefold:First we extend important notions from the Cyclo-Static Dataflow Graph to the Phased Computation Graph model and two equivalent sufficient conditions of liveness.Second, we present a random dataflow graph generator able to generate Synchonous Dataflow Graphs, Cyclo-Static Dataflow Graphs and Phased Computation Graphs. The Generator, is able to generate live dataflow of up to 10,000 tasks in less than 30 seconds. It is compared with SDF3 and PREESM.Third and most important, we propose a new method of evaluation of a mapping using the Synchonous Dataflow Graph and the Cyclo-Static Dataflow Graph models. The method evaluates efficiently the memory footprint of the communications of a dataflow graph mapped on a distributed architecture. The evaluation is declined in two versions, the first guarantees a live mapping while the second accounts for a constraint on throughput.The evaluation method is experimented on dataflow graphs from Turbine and on real-life applications
Vaverka, Filip. "Lokalizace mobilního robota pomocí kamery." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2015. http://www.nusl.cz/ntk/nusl-234985.
Full textRuss, Ricardo. "Service Level Achievments - Test Data for Optimal Service Selection." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-50538.
Full textGao, Pu. "Generation and properties of random graphs and analysis of randomized algorithms." Thesis, 2010. http://hdl.handle.net/10012/4987.
Full textLyu, Yu-Han, and 呂昱翰. "Random Generating k-connected Graphs." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/21237074229415401785.
Full text國立清華大學
資訊工程學系
95
Enumerating combinatorial objects is an important research topic in combinatorics. Algorithms for random generating combinatorial objects are called sampling. Sampling has many applications in computer science, for example program testing. Mathematicians can use sampling to verify combinatorial property. We developed a simple algorithm to sample k-connected graphs based on Markov Chain Monte Carlo method. This algorithm generates graphs at approximately uniform distribution, and we prove it is rapidly mixing
Books on the topic "Random graph generation"
Reggini, Horacio C. Regular polyhedra: Random generation, Hamiltonian paths, and single chain nets. Buenos Aires: Academia Nacional de Ciencias Exactas, Físicas y Naturales, 1991.
Find full textMikov, Aleksandr. Generalized graphs and grammars. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1013698.
Full textCoolen, A. C. C., A. Annibale, and E. S. Roberts. Random graph ensembles. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0003.
Full textCoolen, A. C. C., A. Annibale, and E. S. Roberts. Applications of random graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0011.
Full textCoolen, 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.
Full textCoolen, A. C. C., A. Annibale, and E. S. Roberts. Introduction. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0001.
Full textCoolen, A. C. C., A. Annibale, and E. S. Roberts. Ensembles with hard constraints. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0005.
Full textCoolen, A. C. C., A. Annibale, and E. S. Roberts. Soft constraints: exponential random graph models. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0004.
Full textCoolen, Ton, Alessia Annibale, and Ekaterina Roberts. Generating Random Networks and Graphs. Oxford University Press, 2017.
Find full textCoolen, A. C. C., A. Annibale, and E. S. Roberts. Specific constructions. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.003.0009.
Full textBook chapters on the topic "Random graph generation"
Bach, Benjamin, Andre Spritzer, Evelyne Lutton, and Jean-Daniel Fekete. "Interactive Random Graph Generation with Evolutionary Algorithms." In Graph Drawing, 541–52. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36763-2_48.
Full textTrejo-Sánchez, Joel Antonio, Andrés Vela-Navarro, Alejandro Flores-Lamas, José Luis López-Martínez, Carlos Bermejo-Sabbagh, Nora Leticia Cuevas-Cuevas, and Homero Toral-Cruz. "Fast Random Cactus Graph Generation." In Communications in Computer and Information Science, 129–36. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-10448-1_12.
Full textLeborgne, Aurélie, Marija Kirandjiska, and Florence Le Ber. "Random Generation of a Locally Consistent Spatio-Temporal Graph." In Graph-Based Representation and Reasoning, 155–69. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86982-3_12.
Full textNobari, Sadegh, Qiang Qu, Muhammad Muzammal, and Qingshan Jiang. "Renovating Watts and Strogatz Random Graph Generation by a Sequential Approach." In Web Information Systems Engineering – WISE 2018, 348–63. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02922-7_24.
Full textCanon, Louis-Claude, Mohamad El Sayah, and Pierre-Cyrille Héam. "A Comparison of Random Task Graph Generation Methods for Scheduling Problems." In Lecture Notes in Computer Science, 61–73. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29400-7_5.
Full textOmodeo, Eugenio G., Alberto Policriti, and Alexandru I. Tomescu. "Random Generation of Sets." In On Sets and Graphs, 201–16. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54981-1_7.
Full textRaman, Rajeev. "Generating random graphs efficiently." In Advances in Computing and Information — ICCI '91, 149–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54029-6_164.
Full textTinhofer, G. "Generating Graphs Uniformly at Random." In Computing Supplementum, 235–55. Vienna: Springer Vienna, 1990. http://dx.doi.org/10.1007/978-3-7091-9076-0_12.
Full textŞeker, Oylum, Pinar Heggernes, Tınaz Ekim, and Z. Caner Taşkın. "Linear-Time Generation of Random Chordal Graphs." In Lecture Notes in Computer Science, 442–53. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57586-5_37.
Full textChakraborty, Sumit, Maumita Chakraborty, and Rajat Kumar Pal. "Generation of Simple Undirected Connected Random Graphs." In Lecture Notes on Data Engineering and Communications Technologies, 197–206. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4968-1_16.
Full textConference papers on the topic "Random graph generation"
Nobari, Sadegh, Xuesong Lu, Panagiotis Karras, and Stéphane Bressan. "Fast random graph generation." In the 14th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1951365.1951406.
Full textCordeiro, Daniel, Grégory Mounié, Swann Perarnau, Denis Trystram, Jean-Marc Vincent, and Frédéric Wagner. "Random graph generation for scheduling simulations." In 3rd International ICST Conference on Simulation Tools and Techniques. ICST, 2010. http://dx.doi.org/10.4108/icst.simutools2010.8667.
Full textLiu, Fang, Evercita Eugenio, Ick Hoon Jin, and Claire Bowen. "Differentially Private Generation of Social Networks via Exponential Random Graph Models." In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC). IEEE, 2020. http://dx.doi.org/10.1109/compsac48688.2020.00-11.
Full textMariano, Matheus Monteiro, Érica Ferreira Souza, André Takeshi Endo, and Nandamudi L. Vijaykumar. "A comparative study of algorithms for generating switch cover test sets." In XV Simpósio Brasileiro de Qualidade de Software. Sociedade Brasileira de Computação - SBC, 2016. http://dx.doi.org/10.5753/sbqs.2016.15122.
Full textCaragiannis, Ioannis, and Evanthia Tsitsoka. "Deanonymizing Social Networks Using Structural Information." 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.2019/169.
Full textAl-Ghafees, Mohammed, and James Whittaker. "Markov Chain-based Test Data Adequacy Criteria: a Complete Family." In 2002 Informing Science + IT Education Conference. Informing Science Institute, 2002. http://dx.doi.org/10.28945/2435.
Full textKim, Jeong Han, and Van H. Vu. "Generating random regular graphs." In the thirty-fifth ACM symposium. New York, New York, USA: ACM Press, 2003. http://dx.doi.org/10.1145/780542.780576.
Full textGao, Pu, and Nicholas Wormald. "Uniform Generation of Random Regular Graphs." In 2015 IEEE 56th Annual Symposium on Foundations of Computer Science (FOCS). IEEE, 2015. http://dx.doi.org/10.1109/focs.2015.78.
Full textVullikanti, Anil Kumar S. "Parallel Generation of Large-Scale Random Graphs." In 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE, 2018. http://dx.doi.org/10.1109/ipdpsw.2018.00054.
Full textBayati, Mohsen, Andrea Montanari, and Amin Saberi. "Generating Random Graphs with Large Girth." In Proceedings of the Twentieth Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2009. http://dx.doi.org/10.1137/1.9781611973068.63.
Full textReports on the topic "Random graph generation"
Moseman, Elizabeth. Improving the Computational Efficiency of the Blitzstein-Diaconis Algorithm for Generating Random Graphs of Prescribed Degree. National Institute of Standards and Technology, July 2015. http://dx.doi.org/10.6028/nist.ir.8066.
Full textSchulz, Jan, Daniel Mayerhoffer, and Anna Gebhard. A Network-Based Explanation of Perceived Inequality. Otto-Friedrich-Universität, 2021. http://dx.doi.org/10.20378/irb-49393.
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