Academic literature on the topic 'Graph algorithmics'
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Journal articles on the topic "Graph algorithmics"
Meyer, Ulrich, and Manuel Penschuck. "Large-scale graph generation: Recent results of the SPP 1736 – Part II." it - Information Technology 62, no. 3-4 (May 27, 2020): 135–44. http://dx.doi.org/10.1515/itit-2019-0041.
Full textMihelič, Jurij, and Uroš Čibej. "An experimental evaluation of refinement techniques for the subgraph isomorphism backtracking algorithms." Open Computer Science 11, no. 1 (December 17, 2020): 33–42. http://dx.doi.org/10.1515/comp-2020-0149.
Full textReddy, K. Krishna Mohan, P. Renjith, and N. Sadagopan. "Listing all spanning trees in Halin graphs — sequential and Parallel view." Discrete Mathematics, Algorithms and Applications 10, no. 01 (February 2018): 1850005. http://dx.doi.org/10.1142/s1793830918500052.
Full textBentert, Matthias, Leon Kellerhals, and Rolf Niedermeier. "Fair Short Paths in Vertex-Colored Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (June 26, 2023): 12346–54. http://dx.doi.org/10.1609/aaai.v37i10.26455.
Full textCasteigts, Arnaud, Anne-Sophie Himmel, Hendrik Molter, and Philipp Zschoche. "Finding Temporal Paths Under Waiting Time Constraints." Algorithmica 83, no. 9 (June 4, 2021): 2754–802. http://dx.doi.org/10.1007/s00453-021-00831-w.
Full textJi, Shengwei, Chenyang Bu, Lei Li, and Xindong Wu. "Local Graph Edge Partitioning." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (October 31, 2021): 1–25. http://dx.doi.org/10.1145/3466685.
Full textCappelletti, Luca, Tommaso Fontana, Elena Casiraghi, Vida Ravanmehr, Tiffany J. Callahan, Carlos Cano, Marcin P. Joachimiak, et al. "GRAPE for fast and scalable graph processing and random-walk-based embedding." Nature Computational Science 3, no. 6 (June 26, 2023): 552–68. http://dx.doi.org/10.1038/s43588-023-00465-8.
Full textErgenç Bostanoğlu, Belgin, and Nourhan Abuzayed. "Dynamic frequent subgraph mining algorithms over evolving graphs: a survey." PeerJ Computer Science 10 (October 8, 2024): e2361. http://dx.doi.org/10.7717/peerj-cs.2361.
Full textNikolopoulos, Stavros D., and Leonidas Palios. "Parallel Algorithms for Recognizing P5-free and ${\bar P}_5$-free Weakly Chordal Graphs." Parallel Processing Letters 14, no. 01 (March 2004): 119–29. http://dx.doi.org/10.1142/s0129626404001763.
Full textKHOUSSAINOV, BAKHADYR, JIAMOU LIU, and MIA MINNES. "Unary automatic graphs: an algorithmic perspective." Mathematical Structures in Computer Science 19, no. 1 (February 2009): 133–52. http://dx.doi.org/10.1017/s0960129508007342.
Full textDissertations / Theses on the topic "Graph algorithmics"
Pitois, François. "Recherche de régularités et représentations succinctes de graphes." Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCK021.
Full textIn this thesis, we investigate regularities in graphs and succinct representations of graphs.A regularity, or structure, is a generic term that refers to a set of vertices in a graph with certain properties.Among the most well-known regularities, we can mention cliques, dense subgraphs, communities, modules, and splits.A succinct representation of a graph is a way of describing it that is more efficient than simply listing the different edges of the graph.Searching for regularities enables obtaining succinct representations.Thus, in a first step, we developed a graph compression algorithm that searches for different graph regularities, selects a portion of them, and partitions the graph based on the selected structures.This algorithm provides a succinct description of the graph that is better than some benchmark algorithms.In a second step, we created our own structures, so they are suitable for compression and are easy enough to search for.To do this, we started from a known structure, the split, and generalized it to create the r-split, where r is a fixed integer parameter.We then showed that the set of r-splits of a graph has a global coherence, in the sense that only a polynomial number of them is sufficient to describe all r-splits of the graph.This generalizes a well-known property of splits, for which only a linear number of them is sufficient to recover all the others.We also demonstrated that r-splits can be computed in polynomial time using submodular function optimization algorithms.In a third step, we focused on searching for particular regularities: patterns in ordered graphs.An ordered graph is a graph in which the vertices are ordered from 1 to n.A pattern is a partial ordered subgraph, in the sense that each pair of vertices can be connected either by an edge, a non-edge, or neither.The goal is to fix a pattern P and build an algorithm capable of detecting if P is in any ordered graph given as an input.This problem is polynomial in the size of the graph via exhaustive search.However, is it possible to do better?We were able to show that yes: most three-vertex patterns can be detected in linear time while exhaustive search requires cubic time.Regarding larger patterns, we exhibited classes of patterns that can be detected in subcubic time: outerplanar patterns.By adding additional constraints, we exhibited a class of patterns that can be detected in linear time: these are outerplanar patterns without cycles and non-edges
Dusart, Jérémie. "Graph searches with applications to cocomparability graphs." Paris 7, 2014. http://www.theses.fr/2014PA077048.
Full textA graph search is a mechanism for systematically visiting the vertices of a graph. It has been a fundamental technique in the design of graph algorithms since the eraarly days of computer science. Many of the early search methods were based on Breadth First Search (BFS) or Depth First Search (DFS) and resulted in efficient algorithms for practical problems such as the distance between two vertices, diameter, connectivity, network flows and the recognition of planar graphs. The purpose of this thesis is to studied the graph search. In this thesis, we present general result about graph search in cocomparability grapj, but also a new charactrization of cocomparability graph and apllications of graph search to solve the problem of transitive orientation, maximal chordal subgraph, clique perator and simplicial vertices. A simple and general framework is also presented to capture most of the well known graph search
Bessy, Stéphane. "Some problems in graph theory and graphs algorithmic theory." Habilitation à diriger des recherches, Université Montpellier II - Sciences et Techniques du Languedoc, 2012. http://tel.archives-ouvertes.fr/tel-00806716.
Full textLarsson, Patrik. "Analyzing and adapting graph algorithms for large persistent graphs." Thesis, Linköping University, Department of Computer and Information Science, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15422.
Full textIn this work, the graph database Neo4j developed by Neo Technology is presented together with some of it's functionality when it comes to accessing data as a graph. This type of data access brings the possibility to implement common graph algorithms on top of Neo4j. Examples of such algorithms are presented together with their theoretical backgrounds. These are mainly algorithms for finding shortest paths and algorithms for different graph measures such as centrality measures. The implementations that have been made are presented, as well as complexity analysis and the performance measures performed on them. The conclusions include that Neo4j is well suited for these types of implementations.
Dinh, Trong Hiêu. "Grammaires de graphes et langages formels." Phd thesis, Université Paris-Est, 2011. http://tel.archives-ouvertes.fr/tel-00665732.
Full textMądry, Aleksander. "From graphs to matrices, and back : new techniques for graph algorithms." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/66014.
Full textCataloged from PDF version of thesis.
Includes bibliographical references (p. 181-192).
The growing need to deal efficiently with massive computing tasks prompts us to consider the following question: How well can we solve fundamental optimization problems if our algorithms have to run really quickly? The motivation for the research presented in this thesis stems from addressing the above question in the context of algorithmic graph theory. To pursue this direction, we develop a toolkit that combines a diverse set of modern algorithmic techniques, including sparsification, low-stretch spanning trees, the multiplicative-weights-update method, dynamic graph algorithms, fast Laplacian system solvers, and tools of spectral graph theory. Using this toolkit, we obtain improved algorithms for several basic graph problems including: -- The Maximum s-t Flow and Minimum s-t Cut Problems. We develop a new approach to computing (1 - [epsilon])-approximately maximum s-t flow and (1 + [epsilon])-approximately minimum s-t cut in undirected graphs that gives the fastest known algorithms for these tasks. These algorithms are the first ones to improve the long-standing bound of O(n3/2') running time on sparse graphs; -- Multicommodity Flow Problems. We set forth a new method of speeding up the existing approximation algorithms for multicommodity flow problems, and use it to obtain the fastest-known (1 - [epsilon])-approximation algorithms for these problems. These results improve upon the best previously known bounds by a factor of roughly [omega](m/n), and make the resulting running times essentially match the [omega](mn) "flow-decomposition barrier" that is a natural obstacle to all the existing approaches; -- " Undirected (Multi-)Cut-Based Minimization Problems. We develop a general framework for designing fast approximation algorithms for (multi-)cutbased minimization problems in undirected graphs. Applying this framework leads to the first algorithms for several fundamental graph partitioning primitives, such as the (generalized) sparsest cut problem and the balanced separator problem, that run in close to linear time while still providing polylogarithmic approximation guarantees; -- The Asymmetric Traveling Salesman Problem. We design an O( )- approximation algorithm for the classical problem of combinatorial optimization: the asymmetric traveling salesman problem. This is the first asymptotic improvement over the long-standing approximation barrier of e(log n) for this problem; -- Random Spanning Tree Generation. We improve the bound on the time needed to generate an uniform random spanning tree of an undirected graph.
by Aleksander Mądry.
Ph.D.
Duffy, Christopher. "Homomorphisms of (j, k)-mixed graphs." Thesis, Bordeaux, 2015. http://hdl.handle.net/1828/6601.
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Zhou, Hang. "Graph algorithms : network inference and planar graph optimization." Thesis, Paris, Ecole normale supérieure, 2015. http://www.theses.fr/2015ENSU0016/document.
Full textThis thesis focuses on two topics of graph algorithms. The first topic is network inference. How efficiently can we find an unknown graph using shortest path queries between its vertices? We assume that the graph has bounded degree. In the reconstruction problem, the goal is to find the graph; and in the verification problem, the goal is to check whether a given graph is correct. We provide randomized algorithms based on a Voronoi cell decomposition. Next, we analyze greedy algorithms, and show that they are near-optimal. We also study the problems on special graph classes, prove lower bounds, and study the approximate reconstruction. The second topic is optimization in planar graphs. We study two problems. In the correlation clustering problem, the input is a weighted graph, where every edge has a label of h+i or h−i, indicating whether its endpoints are in the same category or in different categories. The goal is to find a partition of the vertices into categories that tries to respect the labels. In the two-edge-connected augmentation problem, the input is a weighted graph and a subset R of edges. The goal is to produce a minimum-weight subset S of edges, such that for every edge in R, its endpoints are two-edge-connected in the union of R and S. For planar graphs, we reduce correlation clustering to two-edge-connected augmentation, and show that both problems, although they are NP-hard, have a polynomial-time approximation scheme. We build on the brick decomposition technique developed recently
Sussfeld, Duncan. "Identifying remote homology and gene remodelling using network-based approaches." Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASL112.
Full textThe ever-increasing accumulation of genomic and metagenomic data calls for new methodological developments in bioinformatics, in order to characterise evolutionary phenomena as a whole with better accuracy. In particular, some of the canonical methods to study the evolution of genes and gene families may be ill-suited when the relatedness of sequences is only partially supported. For instance, the definition and reconstruction of gene families face the hurdle of remote homology, which falls beneath the detection thresholds of sequence alignments. Likewise, combinatorial mechanisms of evolution, such as gene fusion and gene fission, challenge the purely tree-based representations of gene family evolution. The use of complementary methods based on sequence similarity networks allows us to circumvent some of these shortcomings, by offering a more holistic representation of similarities between genes. The detection and analysis of highly divergent homologues of strongly conserved families in environmental sequence datasets, in particular, is facilitated by iterative homology search protocols based on networks. This iterative mining of metagenomes reveals an immense diversity of environmental variants in these families, diverging from the known diversity in primary sequence as well as in the tertiary structure of the proteins they encode. It is thus able to suggest possible directions of future explorations into microbial dark matter. Furthermore, by factoring in relationships of partial homology between gene sequences, sequence similarity networks allow for a systematic identification of gene fusion and fission events. It thus becomes possible to assess the effects of these processes on the evolution of biological lineages of interest, enabling us for instance to compare the role that they played in the emergence of complex multicellular phenotypes between several such lineages. More generally, these network-based approaches illustrate the benefits of taking a plurality of models into account, in order to study a broader range of evolutionary processes
Slade, Michael L. "A layout algorithm for hierarchical graphs with constraints /." Online version of thesis, 1994. http://hdl.handle.net/1850/11724.
Full textBooks on the topic "Graph algorithmics"
Golumbic, Martin Charles. Algorithmic graph theory and perfect graphs. 2nd ed. Amsterdam: North Holland, 2004.
Find full textGolumbic, Martin Charles. Algorithmic graph theory and perfect graphs. Amsterdam: Elsevier, 2004.
Find full textEvstigneev, V. A. Teorii͡a︡ grafov: Algoritmy obrabotki beskonturnykh grafov. Novosibirsk: "Nauka," Sibirskoe predprii͡a︡tie RAN, 1998.
Find full textBonato, Anthony. The game of cops and robbers on graphs. Providence, R.I: American Mathematical Society, 2011.
Find full textGiuseppe, Di Battista, ed. Graph drawing: Algorithms for the visualization of graphs. Upper Saddle River, N.J: Prentice Hall, 1999.
Find full textGdanskiy, Nikolay. Fundamentals of the theory and algorithms on graphs. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/978686.
Full textEven, Shimon. Graph algorithms. 2nd ed. Cambridge, NY: Cambridge University Press, 2011.
Find full textMcHugh, James A. Algorithmic graph theory. Englewood Cliffs, N.J: Prentice Hall, 1990.
Find full textToshihide, Ibaraki, ed. Algorithmic aspects of graph connectivity. New York: Cambridge University Press, 2008.
Find full textNagamochi, Hiroshi. Algorithmic aspects of graph connectivity. New York: Cambridge University Press, 2008.
Find full textBook chapters on the topic "Graph algorithmics"
Asahiro, Yuichi, Jesper Jansson, Eiji Miyano, Hirotaka Ono, and Sandhya T. P. "Graph Orientation with Edge Modifications." In Frontiers in Algorithmics, 38–50. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-18126-0_4.
Full textWang, Lusheng, Boting Yang, and Zhaohui Zhan. "Constrained Graph Searching on Trees." In Frontiers of Algorithmics, 239–51. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-39344-0_18.
Full textZaroliagis, Christos D. "Implementations and Experimental Studies of Dynamic Graph Algorithms." In Experimental Algorithmics, 229–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36383-1_11.
Full textFomin, Fedor V., Saket Saurabh, and Neeldhara Misra. "Graph Modification Problems: A Modern Perspective." In Frontiers in Algorithmics, 3–6. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19647-3_1.
Full textVaishali, S., M. S. Atulya, and Nidhi Purohit. "Efficient Algorithms for a Graph Partitioning Problem." In Frontiers in Algorithmics, 29–42. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78455-7_3.
Full textZou, Peng, Hui Li, Chunlin Xin, Wencheng Wang, and Binhai Zhu. "Finding Disjoint Dense Clubs in an Undirected Graph." In Frontiers in Algorithmics, 279–88. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39817-4_27.
Full textAbu-Khzam, Faisal N., Michael A. Langston, Amer E. Mouawad, and Clinton P. Nolan. "A Hybrid Graph Representation for Recursive Backtracking Algorithms." In Frontiers in Algorithmics, 136–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14553-7_15.
Full textMertzios, George B., Othon Michail, George Skretas, Paul G. Spirakis, and Michail Theofilatos. "The Complexity of Growing a Graph." In Algorithmics of Wireless Networks, 123–37. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22050-0_9.
Full textHanaka, Tesshu, Yasuaki Kobayashi, and Taiga Sone. "An Optimal Algorithm for Bisection for Bounded-Treewidth Graph." In Frontiers in Algorithmics, 25–36. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59901-0_3.
Full textFürer, Martin. "Efficient Computation of the Characteristic Polynomial of a Threshold Graph." In Frontiers in Algorithmics, 45–51. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19647-3_5.
Full textConference papers on the topic "Graph algorithmics"
Xie, Xinjiu, and Jinxian Zhang. "Link Prediction in Knowledge Graphs for Graph Convolutional Networks." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721627.
Full textLattanzi, Silvio, and Vahab Mirrokni. "Distributed Graph Algorithmics." In WSDM 2015: Eighth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2684822.2697043.
Full textShasha, Dennis, Jason T. L. Wang, and Rosalba Giugno. "Algorithmics and applications of tree and graph searching." In the twenty-first ACM SIGMOD-SIGACT-SIGART symposium. New York, New York, USA: ACM Press, 2002. http://dx.doi.org/10.1145/543613.543620.
Full textBlum, Avrim, T.-H. Hubert Chan, and Mugizi Robert Rwebangira. "A Random-Surfer Web-Graph Model." In 2006 Proceedings of the Third Workshop on Analytic Algorithmics and Combinatorics (ANALCO). Philadelphia, PA: Society for Industrial and Applied Mathematics, 2006. http://dx.doi.org/10.1137/1.9781611972962.8.
Full textDrmota, Michael, and Marc Noy. "Extremal Parameters in Sub-Critical Graph Classes." In 2013 Proceedings of the Tenth Workshop on Analytic Algorithmics and Combinatorics (ANALCO). Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973037.1.
Full textJacquet, Philippe, and Abram Magner. "Variance of Size in Regular Graph Tries." In 2015 Proceedings of the Twelfth Workshop on Analytic Algorithmics and Combinatorics (ANALCO). Philadelphia, PA: Society for Industrial and Applied Mathematics, 2014. http://dx.doi.org/10.1137/1.9781611973761.9.
Full textBóna, Miklós, and Andrew Vince. "The Number of Ways to Assemble a Graph." In 2013 Proceedings of the Tenth Workshop on Analytic Algorithmics and Combinatorics (ANALCO). Philadelphia, PA: Society for Industrial and Applied Mathematics, 2013. http://dx.doi.org/10.1137/1.9781611973037.2.
Full textZhao, Jun, and Panpan Zhang. "On Connectivity in a General Random Intersection Graph." In 2016 Proceedings of the Thirteenth Workshop on Analytic Algorithmics and Combinatorics (ANALCO). Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974324.12.
Full textNguyen, Van, and Chip Martel. "Augmented Graph Models for Small-World Analysis with Geographical Factors." In 2008 Proceedings of the Fifth Workshop on Analytic Algorithmics and Combinatorics (ANALCO). Philadelphia, PA: Society for Industrial and Applied Mathematics, 2008. http://dx.doi.org/10.1137/1.9781611972986.5.
Full textWhidden, Chris, and Frederick A. Matsen. "Ricci-Ollivier Curvature of the Rooted Phylogenetic Subtree-Prune-Regraft Graph." In 2016 Proceedings of the Thirteenth Workshop on Analytic Algorithmics and Combinatorics (ANALCO). Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974324.6.
Full textReports on the topic "Graph algorithmics"
Parekh, Ojas, Yipu Wang, Yang Ho, Cynthia Phillips, Ali Pinar, James Aimone, and William Severa. Neuromorphic Graph Algorithms. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1829422.
Full textHrebeniuk, Bohdan V. Modification of the analytical gamma-algorithm for the flat layout of the graph. [б. в.], December 2018. http://dx.doi.org/10.31812/123456789/2882.
Full textWerner, Eric, and Jonathan Chu. Graph Algorithms on Future Architectures. Fort Belvoir, VA: Defense Technical Information Center, October 2014. http://dx.doi.org/10.21236/ada611678.
Full textMcLendon, William Clarence, III, and Brian Neil Wylie. Graph algorithms in the titan toolkit. Office of Scientific and Technical Information (OSTI), October 2009. http://dx.doi.org/10.2172/1001014.
Full textGEORGIA INST OF TECH ATLANTA. Graph Minors: Structure Theory and Algorithms. Fort Belvoir, VA: Defense Technical Information Center, April 1993. http://dx.doi.org/10.21236/ada266033.
Full textThomas, Robin. Graph Minors: Structure Theory and Algorithms. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada271851.
Full textGil, Oliver Fernández, and Anni-Yasmin Turhan. Answering Regular Path Queries Under Approximate Semantics in Lightweight Description Logics. Technische Universität Dresden, 2020. http://dx.doi.org/10.25368/2022.261.
Full textPlotkin, Serge. Research in Graph Algorithms and Combinatorial Optimization. Fort Belvoir, VA: Defense Technical Information Center, March 1995. http://dx.doi.org/10.21236/ada292630.
Full textSullivan, Blair D., Dinesh P. Weerapurage, and Christopher S. Groer. Parallel Algorithms for Graph Optimization using Tree Decompositions. Office of Scientific and Technical Information (OSTI), June 2012. http://dx.doi.org/10.2172/1042920.
Full textGabow, Harold N., and Robert E. Tarjan. Faster Scaling Algorithms for General Graph Matching Problems. Fort Belvoir, VA: Defense Technical Information Center, April 1989. http://dx.doi.org/10.21236/ada215112.
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