Relevant bibliographies by topics / Large graphs

Contents

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

Academic literature on the topic 'Large graphs'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Large graphs"

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PERARNAU, G., та B. REED. "Existence of Spanning ℱ-Free Subgraphs with Large Minimum Degree". Combinatorics, Probability and Computing 26, № 3 (2016): 448–67. http://dx.doi.org/10.1017/s0963548316000328.

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Let ℱ be a family of graphs and letdbe large enough. For everyd-regular graphG, we study the existence of a spanning ℱ-free subgraph ofGwith large minimum degree. This problem is well understood if ℱ does not contain bipartite graphs. Here we provide asymptotically tight results for many families of bipartite graphs such as cycles or complete bipartite graphs. To prove these results, we study a locally injective analogue of the question.
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FÜRER, MARTIN, and SHIVA PRASAD KASIVISWANATHAN. "Approximately Counting Embeddings into Random Graphs." Combinatorics, Probability and Computing 23, no. 6 (2014): 1028–56. http://dx.doi.org/10.1017/s0963548314000339.

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LetHbe a graph, and letCH(G) be the number of (subgraph isomorphic) copies ofHcontained in a graphG. We investigate the fundamental problem of estimatingCH(G). Previous results cover only a few specific instances of this general problem, for example the case whenHhas degree at most one (the monomer-dimer problem). In this paper we present the first general subcase of the subgraph isomorphism counting problem, which is almost always efficiently approximable. The results rely on a new graph decomposition technique. Informally, the decomposition is a labelling of the vertices such that every edge
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Ábrego, Bernardo M., Ruy Fabila-Monroy, Silvia Fernández-Merchant, et al. "Proximity graphs inside large weighted graphs." Networks 61, no. 1 (2012): 29–39. http://dx.doi.org/10.1002/net.21464.

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Lovász, László. "Very Large Graphs." Current Developments in Mathematics 2008, no. 1 (2008): 67–128. http://dx.doi.org/10.4310/cdm.2008.v2008.n1.a2.

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Hu, Yifan, and Lei Shi. "Visualizing large graphs." Wiley Interdisciplinary Reviews: Computational Statistics 7, no. 2 (2015): 115–36. http://dx.doi.org/10.1002/wics.1343.

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Shao, Zehui, Jin Xu, S. M. Sheikholeslami, and Shaohui Wang. "The Domination Complexity and Related Extremal Values of Large 3D Torus." Complexity 2018 (July 2, 2018): 1–8. http://dx.doi.org/10.1155/2018/3041426.

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Domination is a structural complexity of chemical molecular graphs. A dominating set in a (molecular) graphG=V,Eis a subsetS⊆Vsuch that each vertex inV\Sis adjacent to at least one vertex inS. The domination numberγGof a graphGis the minimum size of a dominating set inG. In this paper, computer-aided approaches for obtaining bounds for domination number on torus graphs are here considered, and many new exact values and bounds are obtained.
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Cheng, Hong, Yang Zhou, and Jeffrey Xu Yu. "Clustering Large Attributed Graphs." ACM Transactions on Knowledge Discovery from Data 5, no. 2 (2011): 1–33. http://dx.doi.org/10.1145/1921632.1921638.

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Kollios, G., M. Potamias, and E. Terzi. "Clustering Large Probabilistic Graphs." IEEE Transactions on Knowledge and Data Engineering 25, no. 2 (2013): 325–36. http://dx.doi.org/10.1109/tkde.2011.243.

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Bollobás, Béla, and Vladimir Nikiforov. "Large joints in graphs." European Journal of Combinatorics 32, no. 1 (2011): 33–44. http://dx.doi.org/10.1016/j.ejc.2010.07.010.

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Hudák, Peter, Mária Maceková, Tomas Madaras, and Pavol Široczki. "Light graphs in planar graphs of large girth." Discussiones Mathematicae Graph Theory 36, no. 1 (2016): 227. http://dx.doi.org/10.7151/dmgt.1847.

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Dissertations / Theses on the topic "Large graphs"

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Le, Tien Nam. "Patterns in Large Graphs." Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEN079/document.

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Un graphe est un ensemble de noeuds, ensemble de liens reliant des paires de noeuds. Avec la quantité accumulée de données collectées, il existe un intérêt croissant pour la compréhension des structures et du comportement de très grands graphes. Néanmoins, l’augmentation rapide de la taille des grands graphes rend l’étude de tous les graphes de moins en moins efficace. Ainsi, il existe une demande impérieuse pour des méthodes plus efficaces pour étudier de grands graphes sans nécessiter la connaissance de tous les graphes. Une méthode prometteuse pour comprendre le comportement de grands graph
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Henry, Tyson Rombauer. "Interactive graph layout: The exploration of large graphs." Diss., The University of Arizona, 1992. http://hdl.handle.net/10150/185833.

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Directed and undirected graphs provide a natural notation for describing many fundamental structures of computer science. Unfortunately graphs are hard to draw in an easy to read fashion. Traditional graph layout algorithms have focused on creating good layouts for the entire graph. This approach works well with smaller graphs, but often cannot produce readable layouts for large graphs. This dissertation presents a novel methodology for viewing large graphs. The basic concept is to allow the user to interactively navigate through large graphs, learning about them in appropriately small and con
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Li, Yifan. "Edge partitioning of large graphs." Thesis, Paris 6, 2017. http://www.theses.fr/2017PA066346/document.

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Dans cette thèse nous étudions un problème fondamental, le partitionnement de graphe, dans le contexte de la croissance rapide des données, le volume des données continues à augmenter, allant des réseaux sociaux à l'internet des objets. En particulier, afin de vaincre les propriétés intraitables existant dans de nombreuses graphies, par exemple, la distribution des degrés en loi de puissance, nous appliquons un nouveau mode pour coupe de sommet, à la place de la méthode traditionnelle (coupe de bord), ainsi que pour assurer une charge de travail équilibrée et raisonnablement dans le traitement
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Larsson, 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.

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<p>In 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
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Zhang, Shijie. "Index-based Graph Querying and Matching in Large Graphs." Cleveland, Ohio : Case Western Reserve University, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=case1263256028.

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Thesis(Ph.D.)--Case Western Reserve University, 2010<br>Title from PDF (viewed on 2010-04-12) Department of Electrical Engineering and Computer Science (EECS) Includes abstract Includes bibliographical references and appendices Available online via the OhioLINK ETD Center
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Das, Sarma Atish. "Algorithms for large graphs." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34709.

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Taylor, Amelia May. "Substructures in large graphs." Thesis, University of Birmingham, 2017. http://etheses.bham.ac.uk//id/eprint/7241/.

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The first problem we address concerns Hamilton cycles. Suppose G is a large digraph in which every vertex has in- and outdegree at least |G|/2. We show that G contains every orientation of a Hamilton cycle except, possibly, the antidirected one. The antidirected case was settled by DeBiasio and Molla. Our result is best possible and improves on an approximate result by Häggkvist and Thomason. We then investigate the random greedy F-free process which was initially studied by Erdős, Suen and Winkler and by Spencer. This process greedily adds edges without creating a copy of F, terminating in a
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Li, Yifan. "Edge partitioning of large graphs." Electronic Thesis or Diss., Paris 6, 2017. https://accesdistant.sorbonne-universite.fr/login?url=https://theses-intra.sorbonne-universite.fr/2017PA066346.pdf.

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Dans cette thèse nous étudions un problème fondamental, le partitionnement de graphe, dans le contexte de la croissance rapide des données, le volume des données continues à augmenter, allant des réseaux sociaux à l'internet des objets. En particulier, afin de vaincre les propriétés intraitables existant dans de nombreuses graphies, par exemple, la distribution des degrés en loi de puissance, nous appliquons un nouveau mode pour coupe de sommet, à la place de la méthode traditionnelle (coupe de bord), ainsi que pour assurer une charge de travail équilibrée et raisonnablement dans le traitement
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Tsalouchidou, Ioanna. "Temporal analysis of large dynamic graphs." Doctoral thesis, Universitat Pompeu Fabra, 2018. http://hdl.handle.net/10803/663755.

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The objective of this thesis is to provide a temporal analysis of the structural and interaction dynamics of large evolving graphs. In this thesis we propose new definitions of important graph metrics in order to include the temporal dimension of the dynamic graphs. We further extend the three important problems of data mining, in the temporal setting. The three problems that we propose are temporal graph summarization, temporal community search and temporal betweenness centrality. Additionally, we propose a distributed version of all our algorithms, that help our techniques to scale u
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Zhang, Yang. "Visually Analyzing Large Scale Graphs." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1439951819.

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Books on the topic "Large graphs"

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Chatterjee, Sourav. Large Deviations for Random Graphs. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65816-2.

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Al-Doujan, Fawwaz Awwad. Small graphs of large girth. University of Birmingham, 1989.

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Chang, Lijun, and Lu Qin. Cohesive Subgraph Computation over Large Sparse Graphs. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03599-0.

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O'Connell, Neil. Some large deviation results for sparse random graphs. Hewlett Packard, 1996.

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Pan, Jeff Z., Guido Vetere, Jose Manuel Gomez-Perez, and Honghan Wu, eds. Exploiting Linked Data and Knowledge Graphs in Large Organisations. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-45654-6.

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Andrei, Neculai. Sparse systems: Digraph approach of large-scale linear systems theory. Verlag TÜV Rheinland, 1985.

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O'Connell, Neil. A large deviation principle for the order of a random permutation. Hewlett Packard, 1996.

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Akin, Cem. Efficient scheduling of real-time compute-intensive periodic graphs on a large grain data flow multiprocessor. Naval Postgraduate School, 1993.

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Muralt, Arnold. The design of switched-capacitor ladder filters: Based on doubly- terminated two-pair signal-flow graphs. Hartung-Gorre Verlag, 1993.

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Sakr, Sherif, Faisal Moeen Orakzai, Ibrahim Abdelaziz, and Zuhair Khayyat. Large-Scale Graph Processing Using Apache Giraph. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47431-1.

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Book chapters on the topic "Large graphs"

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Lazebnik, Felix, and Vasiliy Ustimenko. "Some algebraic constructions of dense graphs of large girth and of large size." In Expanding Graphs. American Mathematical Society, 1993. http://dx.doi.org/10.1090/dimacs/010/07.

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Kılıç, Baran, Can Özturan, and Alper Şen. "Analyzing Large-Scale Blockchain Transaction Graphs for Fraudulent Activities." In Big Data and Artificial Intelligence in Digital Finance. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-94590-9_14.

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AbstractEarly public blockchains provided low transaction throughputs in the range of 7–30 transactions per second. With the emergence of permissioned and proof-of-stake-based blockchains, transaction throughputs are expected to rise drastically to thousands per second. Blockchain transactions form directed graphs. With high transaction throughputs and growing blockchain adoption by banks, businesses and customers in general, the number of edges in transaction graphs will dynamically grow to billions. An analysis of large-scale transaction graphs is needed for tracing fraudulent activities on
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Erciyes, K. "Analysis of Large Graphs." In Texts in Computer Science. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73235-0_13.

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Serratosa, Francesc. "Graph Embedding of Almost Constant Large Graphs." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-49018-7_2.

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Gervacio, Severino V. "Subdivision Number of Large Complete Graphs and Large Complete Multipartite Graphs." In Combinatorial Geometry and Graph Theory. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30540-8_10.

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Mukkamala, Padmini, János Pach, and Deniz Sarıöz. "Graphs with Large Obstacle Numbers." In Graph Theoretic Concepts in Computer Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-16926-7_27.

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Hassan, Zohair Raza, Mudassir Shabbir, Imdadullah Khan, and Waseem Abbas. "Estimating Descriptors for Large Graphs." In Advances in Knowledge Discovery and Data Mining. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-47426-3_60.

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Chatterjee, Sourav. "Large Deviations for Sparse Graphs." In Lecture Notes in Mathematics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65816-2_8.

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Wang, Dong, Lei Zou, Wanqiong Pan, and Dongyan Zhao. "Keyword Graph: Answering Keyword Search over Large Graphs." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35527-1_53.

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Wu, Honghan, Ronald Denaux, Panos Alexopoulos, Yuan Ren, and Jeff Z. Pan. "Understanding Knowledge Graphs." In Exploiting Linked Data and Knowledge Graphs in Large Organisations. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-45654-6_6.

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Conference papers on the topic "Large graphs"

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Zhang, Lizhi, Zhiquan Lai, Feng Liu, and Zhejiang Ran. "ADGraph: Accurate, Distributed Training on Large Graphs." In 8th International Conference on Computer Science and Information Technology (CoSIT 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110408.

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Graph neural networks (GNNs) have been emerging as powerful learning tools for recommendation systems, social networks and knowledge graphs. In these domains, the scale of graph data is immense, so that distributed graph learning is required for efficient GNNs training. Graph partition-based methods are widely adopted to scale the graph training. However, most of the previous works focus on scalability other than the accuracy and are not thoroughly evaluated on large-scale graphs. In this paper, we introduce ADGraph (accurate and distributed training on large graphs), exploring how to improve
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Faralli, Stefano, Irene Finocchi, Simone Paolo Ponzetto, and Paola Velardi. "Efficient Pruning of Large Knowledge Graphs." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/564.

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In this paper we present an efficient and highly accurate algorithm to prune noisy or over-ambiguous knowledge graphs given as input an extensional definition of a domain of interest, namely as a set of instances or concepts. Our method climbs the graph in a bottom-up fashion, iteratively layering the graph and pruning nodes and edges in each layer while not compromising the connectivity of the set of input nodes. Iterative layering and protection of pre-defined nodes allow to extract semantically coherent DAG structures from noisy or over-ambiguous cyclic graphs, without loss of information a
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Ajwani, Deepak, Marcel Karnstedt, and Alessandra Sala. "Processing Large Graphs." In WWW '15: 24th International World Wide Web Conference. ACM, 2015. http://dx.doi.org/10.1145/2740908.2741990.

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Stanton, Isabelle, and Gabriel Kliot. "Streaming graph partitioning for large distributed graphs." In the 18th ACM SIGKDD international conference. ACM Press, 2012. http://dx.doi.org/10.1145/2339530.2339722.

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Mathys, Yves. "A graph browser for large directed graphs." In the 1992 ACM/SIGAPP Symposium. ACM Press, 1992. http://dx.doi.org/10.1145/143559.143678.

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Mehrabian, Abbas, Ankit Anand, Hyunjik Kim, et al. "Finding Increasingly Large Extremal Graphs with AlphaZero and Tabu Search." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/772.

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This work proposes a new learning-to-search benchmark and uses AI to discover new mathematical knowledge related to an open conjecture of Erdos (1975) in extremal graph theory. The problem is to find graphs with a given size (number of nodes) that maximize the number of edges without having 3- or 4-cycles. We formulate this as a sequential decision-making problem and compare AlphaZero, a neural network-guided tree search, with tabu search, a heuristic local search method. Using either method, by introducing a curriculum---jump-starting the search for larger graphs using good graphs found at sm
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Yao, Kai-Lang, and Wu-Jun Li. "Blocking-based Neighbor Sampling for Large-scale Graph Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/455.

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The exponential increase in computation and memory complexity with the depth of network has become the main impediment to the successful application of graph neural networks (GNNs) on large-scale graphs like graphs with hundreds of millions of nodes. In this paper, we propose a novel neighbor sampling strategy, dubbed blocking-based neighbor sampling (BNS), for efficient training of GNNs on large-scale graphs. Specifically, BNS adopts a policy to stochastically block the ongoing expansion of neighboring nodes, which can reduce the rate of the exponential increase in computation and memory comp
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Lin, Jinkun, Shaowei Cai, Chuan Luo, and Kaile Su. "A Reduction based Method for Coloring Very Large Graphs." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/73.

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The graph coloring problem (GCP) is one of the most studied NP hard problems and has numerous applications. Despite the practical importance of GCP, there are limited works in solving GCP for very large graphs. This paper explores techniques for solving GCP on very large real world graphs.We first propose a reduction rule for GCP, which is based on a novel concept called degree bounded independent set.The rule is iteratively executed by interleaving between lower bound computation and graph reduction. Based on this rule, we develop a novel method called FastColor, which also exploits fast cliq
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Jiefeng Cheng, Xianggang Zeng, and J. X. Yu. "Top-k graph pattern matching over large graphs." In 2013 29th IEEE International Conference on Data Engineering (ICDE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icde.2013.6544895.

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Filippidou, Ioanna, and Yannis Kotidis. "Effective and efficient graph augmentation in large graphs." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840681.

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Reports on the topic "Large graphs"

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Chau, Duen H. Data Mining Meets HCI: Making Sense of Large Graphs. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada566568.

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Barooah, Prabir, and Joao P. Hespanha. Estimation from Relative Measurements: Electrical Analogy and Large Graphs. Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada473862.

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Akoglu, Leman, Jilles Vreeken, Hanghang Tong, Duen H. Chau, and Christos Faloutsos. Islands and Bridges: Making Sense of Marked Nodes in Large Graphs. Defense Technical Information Center, 2012. http://dx.doi.org/10.21236/ada566565.

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Akoglu, Leman, Hanghang Tong, Nikolaj Tatti, Jilles Vreeken, Duen H. Chau, and Christos Faloutsos. Islands and Bridges: Making Sense of Marked Nodes in Large Graphs. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada580208.

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Toroczkai, Zoltan. DARPA Ensemble-Based Modeling Large Graphs & Applications to Social Networks. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada627064.

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De Sterck, H. FINAL REPORT (MILESTONE DATE 9/30/13) FOR SUBCONTRACT NO. B603393: "CLUSTERING AND RANDOMIZATION FOR LARGE GRAPHS AND HYPERGRAPHS". Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1093898.

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Kuramochi, Michihiro, and George Karypis. Finding Frequent Patterns in a Large Sparse Graph. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada438928.

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Kyrola, Aapo. Large-scale Graph Computation on Just a PC. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada603410.

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Levine, S. Interactive 3D Motion Graphics with Large Data Bases. Office of Scientific and Technical Information (OSTI), 2018. http://dx.doi.org/10.2172/1453942.

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Roth, Steven F. Intelligent Graphic Interfaces for Displaying Large Amounts of Information. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada300098.

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