Academic literature on the topic 'Hierarchical graphs'
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Journal articles on the topic "Hierarchical graphs"
EADES, PETER, XUEMIN LIN, and ROBERTO TAMASSIA. "AN ALGORITHM FOR DRAWING A HIERARCHICAL GRAPH." International Journal of Computational Geometry & Applications 06, no. 02 (June 1996): 145–55. http://dx.doi.org/10.1142/s0218195996000101.
Full textBUSATTO, GIORGIO, HANS-JÖRG KREOWSKI, and SABINE KUSKE. "Abstract hierarchical graph transformation." Mathematical Structures in Computer Science 15, no. 4 (July 15, 2005): 773–819. http://dx.doi.org/10.1017/s0960129505004846.
Full textKasyanov, V. N. "Methods and Tools for Visualization of Graphs and Graph Algorithms." International Journal of Applied Mathematics and Informatics 15 (November 16, 2021): 78–84. http://dx.doi.org/10.46300/91014.2021.15.13.
Full textFaran, Rachel, and Orna Kupferman. "A Parametrized Analysis of Algorithms on Hierarchical Graphs." International Journal of Foundations of Computer Science 30, no. 06n07 (September 2019): 979–1003. http://dx.doi.org/10.1142/s0129054119400252.
Full textLin, Zhe, Fan Zhang, Xuemin Lin, Wenjie Zhang, and Zhihong Tian. "Hierarchical core maintenance on large dynamic graphs." Proceedings of the VLDB Endowment 14, no. 5 (January 2021): 757–70. http://dx.doi.org/10.14778/3446095.3446099.
Full textSHARMA, ROHAN, BIBHAS ADHIKARI, and TYLL KRUEGER. "SELF-ORGANIZED CORONA GRAPHS: A DETERMINISTIC COMPLEX NETWORK MODEL WITH HIERARCHICAL STRUCTURE." Advances in Complex Systems 22, no. 06 (September 2019): 1950019. http://dx.doi.org/10.1142/s021952591950019x.
Full textEngels, Gregor, and Andy Schürr. "Encapsulated Hierarchical Graphs, Graph Types, and Meta Types." Electronic Notes in Theoretical Computer Science 2 (1995): 101–9. http://dx.doi.org/10.1016/s1571-0661(05)80186-0.
Full textWANG, XIAOQIAN, HUILING XU, and CHANGBING MA. "CONSENSUS PROBLEMS IN WEIGHTED HIERARCHICAL GRAPHS." Fractals 27, no. 06 (September 2019): 1950086. http://dx.doi.org/10.1142/s0218348x19500865.
Full textOggier, Frédérique, and Anwitaman Datta. "Renyi entropy driven hierarchical graph clustering." PeerJ Computer Science 7 (February 25, 2021): e366. http://dx.doi.org/10.7717/peerj-cs.366.
Full textKrinkin, Kirill, Alexander Ivanovich Vodyaho, Igor Kulikov, and Nataly Zhukova. "Deductive Synthesis of Networks Hierarchical Knowledge Graphs." International Journal of Embedded and Real-Time Communication Systems 12, no. 3 (July 2021): 32–48. http://dx.doi.org/10.4018/ijertcs.2021070103.
Full textDissertations / Theses on the topic "Hierarchical graphs"
Busatto, Giorgio. "An abstract model of hierarchical graphs and hierarchical graph transformation." Oldenburg : Univ., Fachbereich Informatik, 2002. http://deposit.ddb.de/cgi-bin/dokserv?idn=967851955.
Full textBusatto, Giorgio [Verfasser]. "An abstract model of hierarchical graphs and hierarchical graph transformation / von Giorgio Busatto." Oldenburg : Univ., Fachbereich Informatik, 2002. http://d-nb.info/967851955/34.
Full textDorrian, Henry Joseph. "Hierarchical graphs and oscillator dynamics." Thesis, Manchester Metropolitan University, 2015. http://e-space.mmu.ac.uk/580120/.
Full textSlade, Michael L. "A layout algorithm for hierarchical graphs with constraints /." Online version of thesis, 1994. http://hdl.handle.net/1850/11724.
Full textPuch-Solis, Roberto O. "Hierarchical junction trees." Thesis, University of Warwick, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365243.
Full textReynolds, Jason. "A hierarchical layout algorithm for drawing directed graphs." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq20694.pdf.
Full textWallgrün, Jan Oliver. "Hierarchical Voronoi graphs spatial representation and reasoning for mobile robots." Berlin Heidelberg Springer, 2008. http://d-nb.info/99728210X/04.
Full textSpisla, Christiane [Verfasser]. "Compaction of Orthogonal and Hierarchical Graph Drawings Using Constraint Graphs and Minimum Cost Flows / Christiane Spisla." München : Verlag Dr. Hut, 2019. http://d-nb.info/119641467X/34.
Full textSantana, Maia Deise. "A study of hierarchical watersheds on graphs with applications to image segmentation." Thesis, Paris Est, 2019. http://www.theses.fr/2019PESC2069.
Full textThe wide literature on graph theory invites numerous problems to be modeled in the framework of graphs. In particular, clustering and segmentation algorithms designed this framework can be applied to solve problems in various domains, including image processing, which is the main field of application investigated in this thesis. In this work, we focus on a semi-supervised segmentation tool widely studied in mathematical morphology and used in image analysis applications, namely the watershed transform. We explore the notion of a hierarchical watershed, which is a multiscale extension of the notion of watershed allowing to describe an image or, more generally, a dataset with partitions at several detail levels. The main contributions of this study are the following : - Recognition of hierarchical watersheds : we propose a characterization of hierarchical watersheds which leads to an efficient algorithm to determine if a hierarchy is a hierarchical watershed of a given edge-weighted graph. - Watersheding operator : we introduce the watersheding operator, which, given an edge-weighted graph, maps any hierarchy of partitions into a hierarchical watershed of this edge-weighted graph. We show that this operator is idempotent and its fixed points are the hierarchical watersheds. We also propose an efficient algorithm to compute the result of this operator. - Probability of hierarchical watersheds : we propose and study a notion of probability of hierarchical watersheds, and we design an algorithm to compute the probability of a hierarchical watershed. Furthermore, we present algorithms to compute the hierarchical watersheds of maximal and minimal probabilities of a given weighted graph. - Combination of hierarchies : we investigate a family of operators to combine hierarchies of partitions and study the properties of these operators when applied to hierarchical watersheds. In particular, we prove that, under certain conditions, the family of hierarchical watersheds is closed for the combination operator. - Evaluation of hierarchies : we propose an evaluation framework of hierarchies, which is further used to assess hierarchical watersheds and combinations of hierarchies. In conclusion, this thesis reviews existing and introduces new properties and algorithms related to hierarchical watersheds, showing the theoretical richness of this framework and providing insightful view for its applications in image analysis and computer vision and, more generally, for data processing and machine learning
Charpentier, Bertrand. "Multi-scale clustering in graphs using modularity." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-244847.
Full textDenna avhandling ger en ny hierarkisk klusteralgoritm för grafer, som heter Paris, vilket kan tolkas av modularitetsresultatet och dess upplösningsparameter. Algoritmen är agglomerativ och är baserad på ett enda avstånd mellan kluster som induceras av sannolikheten för sampling av nodpar. Det försöker att approximera de optimala partitionerna vid vilken upplösning som helst i en körning. Förutom en hierarkisk algoritm föreslår denna avhandling fyra algoritmer som beräknar rankningar av de bästa grupperna, kluster och resolutioner genom att bearbeta hierarkiproduktionen i Paris. Dessa algoritmer bygger på ett nytt koncept av klusterstabilitet, kallad sharpscore. Viktiga resultat av dessa fyra algoritmer är förmågan att rangordna kluster, upptäcka bästa klusterskala, gå utöver upplösningsgränsen och upptäcka de mest relevanta resolutionerna. Alla dessa algoritmer har testats på både syntetiska och verkliga datamängder för att illustrera effektiviteten i deras metoder.
Books on the topic "Hierarchical graphs"
Wallgrün, Jan Oliver. Hierarchical Voronoi Graphs. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10345-2.
Full textMikov, Aleksandr. Generalized graphs and grammars. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1013698.
Full textWallgrün, Jan Oliver. Hierarchical Voronoi graphs: Spatial representation and reasoning for mobile robots. Heidelberg: Springer, 2010.
Find full textComputer Systems Laboratory (U.S.), ed. Programmer's Hierarchical Interactive Graphics System (PHIGS). Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, Computer Systems Laboratory, 1995.
Find full textBos, Jan van den, 1939-, ed. 3D interactive computer graphics: The hierarchical modelling system HIRASP. New York: Ellis Horwood, 1990.
Find full textWallgrün, Jan Oliver. Hierarchical Voronoi Graphs: Spatial Representation and Reasoning for Mobile Robots. Springer, 2010.
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 textProgrammer's Hierarchical Interactive Graphics System by Example. Springer Verlag, 1991.
Find full textYust, Jason. Graph Theory for Temporal Structure. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190696481.003.0014.
Full textFarin, Gerald, Bernd Hamann, and Hans Hagen. Hierarchical and Geometrical Methods in Scientific Visualization. Springer, 2011.
Find full textBook chapters on the topic "Hierarchical graphs"
Wallgrün, Jan Oliver. "Introduction." In Hierarchical Voronoi Graphs, 1–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10345-2_1.
Full textWallgrün, Jan Oliver. "Robot Mapping." In Hierarchical Voronoi Graphs, 11–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10345-2_2.
Full textWallgrün, Jan Oliver. "Voronoi-Based Spatial Representations." In Hierarchical Voronoi Graphs, 45–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10345-2_3.
Full textWallgrün, Jan Oliver. "Simplification and Hierarchical Voronoi Graph Construction." In Hierarchical Voronoi Graphs, 59–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10345-2_4.
Full textWallgrün, Jan Oliver. "Voronoi Graph Matching for Data Association." In Hierarchical Voronoi Graphs, 85–111. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10345-2_5.
Full textWallgrün, Jan Oliver. "Global Mapping: Minimal Route Graphs Under Spatial Constraints." In Hierarchical Voronoi Graphs, 113–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10345-2_6.
Full textWallgrün, Jan Oliver. "Experimental Evaluation." In Hierarchical Voronoi Graphs, 147–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10345-2_7.
Full textWallgrün, Jan Oliver. "Conclusions and Outlook." In Hierarchical Voronoi Graphs, 177–86. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10345-2_8.
Full textPalomo, E. J., J. M. Ortiz-de-Lazcano-Lobato, Domingo López-Rodríguez, and R. M. Luque. "Hierarchical Graphs for Data Clustering." In Lecture Notes in Computer Science, 432–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02478-8_54.
Full textFernández-Baca, David, and Mark A. Williams. "On matroids and hierarchical graphs." In SWAT 90, 320–31. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/3-540-52846-6_101.
Full textConference papers on the topic "Hierarchical graphs"
Li, Chong, Kunyang Jia, Dan Shen, C. J. Richard Shi, and Hongxia Yang. "Hierarchical Representation Learning for Bipartite Graphs." 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.2019/398.
Full textZhou, Kaixiong, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu. "Multi-Channel Graph Neural Networks." 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.2020/188.
Full textIsmaeel, Alaa A. K., Pradyumn Kumar Shukla, and Hartmut Schmeck. "Dynamic Drawing of Hierarchical Graphs." In Annual International Conference on Computational Mathematics, Computational Geometry & Statistics. Global Science and Technology Forum (GSTF), 2012. http://dx.doi.org/10.5176/2251-1911_cmcgs52.
Full textZhang, Ying, Lu Qin, Fan Zhang, and Wenjie Zhang. "Hierarchical Decomposition of Big Graphs." In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019. http://dx.doi.org/10.1109/icde.2019.00240.
Full textPandey, Prashant, Brian Wheatman, Helen Xu, and Aydin Buluc. "Terrace: A Hierarchical Graph Container for Skewed Dynamic Graphs." In SIGMOD/PODS '21: International Conference on Management of Data. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3448016.3457313.
Full textLeon, Leissi M. Castañeda, Krzysztof Chris Ciesielski, and Paulo A. Vechiatto Miranda. "An Efficient Hierarchical Layered Graph Approach for Multi-Region Segmentation." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8301.
Full textWang, Bin, Teruaki Hayashi, and Yukio Ohsawa. "Hierarchical Graph Convolutional Network for Data Evaluation of Dynamic Graphs." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377789.
Full textChua, Freddy Chong Tat, and Ee-Peng Lim. "Modeling Bipartite Graphs Using Hierarchical Structures." In 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2011). IEEE, 2011. http://dx.doi.org/10.1109/asonam.2011.45.
Full textGordeev, Dmitrii Stanislavovich. "Visualization and debugging on internal representation graph of Cloud-Sisal programs." In 31th International Conference on Computer Graphics and Vision. Keldysh Institute of Applied Mathematics, 2021. http://dx.doi.org/10.20948/graphicon-2021-1-54-62.
Full textWang, Hanchen, Defu Lian, Ying Zhang, Lu Qin, and Xuemin Lin. "GoGNN: Graph of Graphs Neural Network for Predicting Structured Entity Interactions." 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.2020/183.
Full textReports on the topic "Hierarchical graphs"
Tripakis, Stavros, Dai Bui, Bert Rodiers, and Edward A. Lee. Compositionality in Synchronous Data Flow: Modular Code Generation from Hierarchical SDF Graphs. Fort Belvoir, VA: Defense Technical Information Center, October 2009. http://dx.doi.org/10.21236/ada538756.
Full textMathuria, Aakanksha. Approximate Pattern Matching using Hierarchical Graph Construction and Sparse Distributed Representation. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7453.
Full textWells, Aaron, Tracy Christopherson, Gerald Frost, Matthew Macander, Susan Ives, Robert McNown, and Erin Johnson. Ecological land survey and soils inventory for Katmai National Park and Preserve, 2016–2017. National Park Service, September 2021. http://dx.doi.org/10.36967/nrr-2287466.
Full textFederal Information Processing Standards Publication: programmer's hierarchial interactive graphics sytem (PHIGS). Gaithersburg, MD: National Institute of Standards and Technology, 1995. http://dx.doi.org/10.6028/nist.fips.153-1.
Full textUser's guide for the Programmer's hierarchical interactive graphics system (PHIGS) C binding validation tests (version 2). Gaithersburg, MD: National Institute of Standards and Technology, 1993. http://dx.doi.org/10.6028/nist.ir.5238.
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