Relevant bibliographies by topics / Large graph

Academic literature on the topic 'Large graph'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Large graph"

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Ji, 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.

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Graph edge partitioning, which is essential for the efficiency of distributed graph computation systems, divides a graph into several balanced partitions within a given size to minimize the number of vertices to be cut. Existing graph partitioning models can be classified into two categories: offline and streaming graph partitioning models. The former requires global graph information during the partitioning, which is expensive in terms of time and memory for large-scale graphs. The latter creates partitions based solely on the received graph information. However, the streaming model may result in a lower partitioning quality compared with the offline model. Therefore, this study introduces a Local Graph Edge Partitioning model, which considers only the local information (i.e., a portion of a graph instead of the entire graph) during the partitioning. Considering only the local graph information is meaningful because acquiring complete information for large-scale graphs is expensive. Based on the Local Graph Edge Partitioning model, two local graph edge partitioning algorithms—Two-stage Local Partitioning and Adaptive Local Partitioning—are given. Experimental results obtained on 14 real-world graphs demonstrate that the proposed algorithms outperform rival algorithms in most tested cases. Furthermore, the proposed algorithms are proven to significantly improve the efficiency of the real graph computation system GraphX.
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Burch, Michael. "Visual analytics of large dynamic digraphs." Information Visualization 16, no. 3 (August 3, 2016): 167–78. http://dx.doi.org/10.1177/1473871616661194.

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In this article, we investigate the problem of visually representing and analyzing large dynamic directed graphs that consist of many vertices, edges, and time steps. With this work we do not primarily focus on graph details but more on achieving an overview about long graph sequences with the major focus to be scalable in vertex, edge, and time dimensions. To reach this goal, we first map each graph to a bipartite layout with vertices in the same order for each graph supporting a preservation of the viewer’s mental map. A sequence of graphs is placed in a left-to-right reading direction. To further reduce link crossings, we draw partial links with user-definable lengths and finally apply edge splatting as a concept to emphasize graph structures by color coding the generated density fields. Time-varying visual patterns can be recognized by inspecting the changes in the color coding in certain regions in the display. We illustrate the usefulness of the approach in two case studies investigating call graphs changing during software development with 21 releases which is a rather short graph sequence but contains several thousand vertices and edges. Visual scalability in the time dimension is shown with more than 1000 graphs from a dynamic social network dataset consisting of face-to-face contacts acquired during the Hypertext 2009 conference recorded by radio-frequency identification badges.
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Aristoff, David, and Charles Radin. "Emergent Structures in Large Networks." Journal of Applied Probability 50, no. 3 (September 2013): 883–88. http://dx.doi.org/10.1239/jap/1378401243.

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We consider a large class of exponential random graph models and prove the existence of a region of parameter space corresponding to the emergent multipartite structure, separated by a phase transition from a region of disordered graphs. An essential feature is the formalism of graph limits as developed by Lovász et al. for dense random graphs.
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Aristoff, David, and Charles Radin. "Emergent Structures in Large Networks." Journal of Applied Probability 50, no. 03 (September 2013): 883–88. http://dx.doi.org/10.1017/s0021900200009918.

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We consider a large class of exponential random graph models and prove the existence of a region of parameter space corresponding to the emergent multipartite structure, separated by a phase transition from a region of disordered graphs. An essential feature is the formalism of graph limits as developed by Lovász et al. for dense random graphs.
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Wichianpaisarn, Tanawat, and Chariya Uiyyasathian. "Graphs with large clique-chromatic numbers." Discrete Mathematics, Algorithms and Applications 07, no. 04 (December 2015): 1550055. http://dx.doi.org/10.1142/s179383091550055x.

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The clique-chromatic number of a graph [Formula: see text], [Formula: see text], is the least number of colors on [Formula: see text] without a monocolored maximal clique of size at least two. If [Formula: see text] is triangle-free, [Formula: see text]; we then consider only graphs with a triangle. Unlike the chromatic number, the clique-chromatic number of a graph is not necessary to be at least those of its subgraphs. Thus, for any family of graphs [Formula: see text], the boundedness of [Formula: see text][Formula: see text] has been investigated. Many families of graphs are proved to have a bounded set of clique-chromatic numbers. In literature, only few families of graphs are shown to have an unbounded set of clique-chromatic numbers, for instance, the family of line graphs. This paper gives another family of graphs with such an unbounded set. These graphs are obtained by the well-known Mycielski’s construction with a certain property of the initial graph.
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Wong, Pak Chung, Harlan Foote, Patrick Mackey, George Chin, Heidi Sofia, and Jim Thomas. "A Dynamic Multiscale Magnifying Tool for Exploring Large Sparse Graphs." Information Visualization 7, no. 2 (April 17, 2008): 105–17. http://dx.doi.org/10.1057/palgrave.ivs.9500177.

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We present an information visualization tool, known as GreenMax, to visually explore large small-world graphs with up to a million graph nodes on a desktop computer. A major motivation for scanning a small-world graph in such a dynamic fashion is the demanding goal of identifying not just the well-known features but also the unknown–known and unknown–unknown features of the graph. GreenMax uses a highly effective multilevel graph drawing approach to pre-process a large graph by generating a hierarchy of increasingly coarse layouts that later support the dynamic zooming of the graph. This paper describes the graph visualization challenges, elaborates our solution, and evaluates the contributions of GreenMax in the larger context of visual analytics on large small-world graphs. We report the results of two case studies using GreenMax and the results support our claim that we can use GreenMax to locate unexpected features or structures behind a graph.
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Ferber, Asaf, Kyle Luh, and Oanh Nguyen. "Embedding large graphs into a random graph." Bulletin of the London Mathematical Society 49, no. 5 (July 10, 2017): 784–97. http://dx.doi.org/10.1112/blms.12066.

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Ma, Yuliang, Ye Yuan, Meng Liu, Guoren Wang, and Yishu Wang. "Graph simulation on large scale temporal graphs." GeoInformatica 24, no. 1 (November 30, 2019): 199–220. http://dx.doi.org/10.1007/s10707-019-00381-y.

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Wagenpfeil, Stefan, Binh Vu, Paul Mc Kevitt, and Matthias Hemmje. "Fast and Effective Retrieval for Large Multimedia Collections." Big Data and Cognitive Computing 5, no. 3 (July 22, 2021): 33. http://dx.doi.org/10.3390/bdcc5030033.

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The indexing and retrieval of multimedia content is generally implemented by employing feature graphs. These graphs typically contain a significant number of nodes and edges to reflect the level of detail in feature detection. A higher level of detail increases the effectiveness of the results, but also leads to more complex graph structures. However, graph traversal-based algorithms for similarity are quite inefficient and computationally expensive, especially for large data structures. To deliver fast and effective retrieval especially for large multimedia collections and multimedia big data, an efficient similarity algorithm for large graphs in particular is desirable. Hence, in this paper, we define a graph projection into a 2D space (Graph Code) and the corresponding algorithms for indexing and retrieval. We show that calculations in this space can be performed more efficiently than graph traversals due to the simpler processing model and the high level of parallelization. As a consequence, we demonstrate experimentally that the effectiveness of retrieval also increases substantially, as the Graph Code facilitates more levels of detail in feature fusion. These levels of detail also support an increased trust prediction, particularly for fused social media content. In our mathematical model, we define a metric triple for the Graph Code, which also enhances the ranked result representations. Thus, Graph Codes provide a significant increase in efficiency and effectiveness, especially for multimedia indexing and retrieval, and can be applied to images, videos, text and social media information.
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El Moussawi, Adnan, Nacera Bennacer Seghouani, and Francesca Bugiotti. "BGRAP: Balanced GRAph Partitioning Algorithm for Large Graphs." Journal of Data Intelligence 2, no. 2 (June 2021): 116–35. http://dx.doi.org/10.26421/jdi2.2-2.

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The definition of effective strategies for graph partitioning is a major challenge in distributed environments since an effective graph partitioning allows to considerably improve the performance of large graph data analytics computations. In this paper, we propose a multi-objective and scalable Balanced GRAph Partitioning (\algo) algorithm, based on Label Propagation (LP) approach, to produce balanced graph partitions. \algo defines a new efficient initialization procedure and different objective functions to deal with either vertex or edge balance constraints while considering edge direction in graphs. \algo is implemented of top of the open source distributed graph processing system Giraph. The experiments are performed on various graphs with different structures and sizes (going up to 50.6M vertices and 1.9B edges) while varying the number of partitions. We evaluate \algo using several quality measures and the computation time. The results show that \algo (i) provides a good balance while reducing the cuts between the different computed partitions (ii) reduces the global computation time, compared to LP-based algorithms.
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Dissertations / Theses on the topic "Large 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 concise pieces. The motivation of this approach is that large graphs contain too much information to be conveyed by a single canonical layout. For a user to be able to understand the data encoded in the graph she must be able to carve up the graph into manageable pieces and then create custom layouts that match her current interests. An architecture is presented that supports graph exploration. It contains three new concepts for supporting interactive graph layout: interactive decomposition of large graphs, end-user specified layout algorithms, and parameterized layout algorithms. The mechanism for creating custom layout algorithms provides the non-programming end-user with the power to create custom layouts that are well suited for the graph at hand. New layout algorithms are created by combining existing algorithms in a hierarchical structure. This method allows the user to create layouts that accurately reflect the current data set and her current interests. In order to explore a large graph, the user must be able to break the graph into small, more manageable pieces. A methodology is presented that allows the user to apply graph traversal algorithms to large graphs to carve out reasonably sized pieces. Graph traversal algorithms can be combined using a visual programming language. This provides the user with the control to select subgraphs that are of particular interest to her. The ability to Parameterize layout algorithms provides the user with control over the layout process. The user can customize the generated layout by changing parameters to the layout algorithm. Layout algorithm parameterization is placed into an interactive framework that allows the user to iteratively fine tune the generated layout. As a proof of concept, examples are drawn from a working prototype that incorporates this methodology.
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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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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 performed on them. The conclusions include that Neo4j is well suited for these types of implementations.

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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
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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McConville, Ryan. "Clustering algorithms for large scale graph data." Thesis, Queen's University Belfast, 2017. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.727648.

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Yekollu, Srikar. "Graph Based Regularization of Large Covariance Matrices." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1237243768.

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Betkaoui, Brahim. "Reconfigurable computing for large-scale graph traversal algorithms." Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/25049.

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This thesis proposes a reconfigurable computing approach for supporting parallel processing in large-scale graph traversal algorithms. Our approach is based on a reconfigurable hardware architecture which exploits the capabilities of both FPGAs (Field-Programmable Gate Arrays) and a multi-bank parallel memory subsystem. The proposed methodology to accelerate graph traversal algorithms has been applied to three case studies, revealing that application-specific hardware customisations can benefit performance. A summary of our four contributions is as follows. First, a reconfigurable computing approach to accelerate large-scale graph traversal algorithms. We propose a reconfigurable hardware architecture which decouples computation and communication while keeping multiple memory requests in flight at any given time, taking advantage of the high bandwidth of multi-bank memory subsystems. Second, a demonstration of the effectiveness of our approach through two case studies: the breadth-first search algorithm, and a graphlet counting algorithm from bioinformatics. Both case studies involve graph traversal, but each of them adopts a different graph data representation. Third, a method for using on-chip memory resources in FPGAs to reduce off-chip memory accesses for accelerating graph traversal algorithms, through a case-study of the All-Pairs Shortest-Paths algorithm. This case study has been applied to process human brain network data. Fourth, an evaluation of an approach based on instruction-set extension for FPGA design against many-core GPUs (Graphics Processing Units), based on a set of benchmarks with different memory access characteristics. It is shown that while GPUs excel at streaming applications, the proposed approach can outperform GPUs in applications with poor locality characteristics, such as graph traversal problems.
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Larsson, Carl-Johan. "Movie Recommendation System Using Large Scale Graph-Processing." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200601.

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Swartz, Eric Allen. "2-arc transitive polygonal graphs of large girth and valency." The Ohio State University, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=osu1243923530.

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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 up to million vertices. We, finally, evaluate the validity of our methods in terms of efficiency and effectiveness with extensive experimentation on large-scale real-world graphs.
L’objectiu d’aquesta tesi és proporcionar una anàlisi temporal de l'evolució estructural i d’interacció de grans gràfics dinàmics. En aquesta tesi proposem noves definicions de mètriques de gràfiques importants per tal d’incloure la dimensió temporal dels gràfics dinàmics. Ampliem tres problemes importants de mineria de dades en gràfics per a un entorn temporal. Els tres problemes són el resum de gràfics temporals, la cerca temporal de comunitats i la centralitat temporal dels gràfics. A més, proposem una versió distribuïda de tots els nostres algoritmes, que ajuden a les nostres tècniques a escalar fins a milions de vèrtexs. Finalment, avaluem la validesa dels nostres mètodes en termes d’eficiència i eficàcia amb una àmplia experimentació en gràfics del món real a gran escala.
El objetivo de esta tesis es proporcionar un análisis temporal de las dinámicas estructurales y de interacción de grafos masivos dinámicos. Para esto proponemos nuevas definiciones de métricas en grafos importantes para incluir la dimensión temporal de los grafos dinámicos. Además, ampliamos tres problemas importantes de minería de datos en un contexto temporal. Ellos son los resúmenes de grafos temporales, la búsqueda de comunidades en un contexto temporal y la centralidad temporal en grafos. Además, proponemos una versión distribuida de todos nuestros algoritmos, que permiten que nuestras técnicas a escalar hasta millones de vértices. Finalmente, evaluamos la validez de nuestros métodos en términos de eficiencia y efectividad con extensos experimentos en gráfos de gran escala en el mundo real.
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Yuan, Wenjun, and 袁文俊. "Flexgraph: flexible subgraph search in large graphs." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B46087539.

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Books on the topic "Large graph"

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Large networks and graph limits. Providence, Rhode Island: American Mathematical Society, 2012.

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O'Connell, Neil. Some large deviation results for sparse random graphs. Bristol [England]: Hewlett Packard, 1996.

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

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Shao, Yingxia, Bin Cui, and Lei Chen. Large-scale Graph Analysis: System, Algorithm and Optimization. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3928-2.

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Manning, James. Large Graph Pad. The Sketchbook, Sketch Pad, Art Book, Drawing Pape, 2018.

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Zasimowicz. Hexagonal Graph Paper: Large. Independently Published, 2020.

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ZASIMOWICZ. Hexagonal Graph Paper: Large. Independently Published, 2020.

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ZASIMOWICZ. Hexagonal Graph Paper: Large. Independently Published, 2020.

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ZASIMOWICZ. Hexagonal Graph Paper: Large. Independently Published, 2020.

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ZASIMOWICZ. Hexagonal Graph Paper: Large. Independently Published, 2020.

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Book chapters on the topic "Large graph"

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Erciyes, K. "Large Graph Analysis." In Undergraduate Topics in Computer Science, 171–98. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-87886-3_10.

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Geyer, Markus, Michael Kaufmann, and Robert Krug. "Visualizing Differences between Two Large Graphs." In Graph Drawing, 393–94. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18469-7_38.

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Nguyen, Quan, Peter Eades, Seok-Hee Hong, and Weidong Huang. "Large Crossing Angles in Circular Layouts." In Graph Drawing, 397–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18469-7_40.

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Dwyer, Tim, and Lev Nachmanson. "Fast Edge-Routing for Large Graphs." In Graph Drawing, 147–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11805-0_15.

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Batagelj, Vladimir, and Andrej Mrvar. "Pajek— Analysis and Visualization of Large Networks." In Graph Drawing, 477–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45848-4_54.

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Wills, Graham J. "NicheWorks — Interactive visualization of very large graphs." In Graph Drawing, 403–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/3-540-63938-1_85.

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Tee Teoh, Soon, and Ma Kwan-Liu. "RINGS: A Technique for Visualizing Large Hierarchies." In Graph Drawing, 268–75. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36151-0_25.

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Kornaropoulos, Evgenios M., and Ioannis G. Tollis. "DAGView: An Approach for Visualizing Large Graphs." In Graph Drawing, 499–510. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36763-2_44.

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Dumitrescu, Adrian, János Pach, and Géza Tóth. "Drawing Hamiltonian Cycles with No Large Angles." In Graph Drawing, 3–14. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-11805-0_3.

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Batagelj, Vladimir, Andrej Mrvar, and Matjaž Zaveršnik. "Partitioning Approach to Visualization of Large Graphs." In Graph Drawing, 90–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-46648-7_9.

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Conference papers on the topic "Large graph"

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Stanton, Isabelle, and Gabriel Kliot. "Streaming graph partitioning for large distributed graphs." In the 18th ACM SIGKDD international conference. New York, New York, USA: 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. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/143559.143678.

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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}. California: 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 complexity of GNNs. Furthermore, a reweighted policy is applied to graph convolution, to adjust the contribution of blocked and non-blocked neighbors to central nodes. We theoretically prove that BNS provides an unbiased estimation for the original graph convolution operation. Extensive experiments on three benchmark datasets show that, on large-scale graphs, BNS is 2X~5X faster than state-of-the-art methods when achieving the same accuracy. Moreover, even on the small-scale graphs, BNS also demonstrates the advantage of low time cost.
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Faloutsos, Christos. "Large graph mining." In the 23rd international conference. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2566486.2576889.

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Dong, Minjing, Hanting Chen, Yunhe Wang, and Chang Xu. "Crafting Efficient Neural Graph of Large Entropy." 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.2019/311.

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Network pruning is widely applied to deep CNN models due to their heavy computation costs and achieves high performance by keeping important weights while removing the redundancy. Pruning redundant weights directly may hurt global information flow, which suggests that an efficient sparse network should take graph properties into account. Thus, instead of paying more attention to preserving important weight, we focus on the pruned architecture itself. We propose to use graph entropy as the measurement, which shows useful properties to craft high-quality neural graphs and enables us to propose efficient algorithm to construct them as the initial network architecture. Our algorithm can be easily implemented and deployed to different popular CNN models and achieve better trade-offs.
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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 accuracy while keeping large-scale graph training scalability. Firstly, to maintain complete neighbourhood information of the training nodes after graph partitioning, we assign l-hop neighbours of the training nodes to the same partition. We also analyse the accuracy and runtime performance of graph training, with different l-hop settings. Secondly, multi-layer neighbourhood sampling is performed on each partition, so that the mini-batch generated can accurately train target nodes. We study the relationship between convergence accuracy and the sampled layers. We also find that partial neighbourhood sampling can achieve better performance than full neighbourhood sampling. Thirdly, to further overcome the generalization error caused by large-batch training, we choose to reduce batchsize after graph partitioned and apply the linear scaling rule in distributed optimization. We evaluate ADGraph using GraphSage and GAT models with ogbn-products and Reddit datasets on 32 GPUs. Experimental results show that ADGraph achieves better performance than the benchmark accuracy of GraphSage and GAT, while getting 24-29 times speedup on 32 GPUs.
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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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Chen, Zhihuai, Qian Li, Xiaoming Sun, Lirong Xia, and Jialin Zhang. "Approximate Single-Peakedness in Large Elections." In 2020 IEEE International Conference on Knowledge Graph (ICKG). IEEE, 2020. http://dx.doi.org/10.1109/icbk50248.2020.00068.

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Tsapanos, Nikolaos, Anastasios Tefas, Nikolaos Nikolaidis, and Ioannis Pitas. "Large graph clustering using DCT-based graph clustering." In 2014 IEEE Symposium on Computational Intelligence in Big Data (CIBD). IEEE, 2014. http://dx.doi.org/10.1109/cibd.2014.7011536.

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Reports on the topic "Large graph"

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

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

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, Hang Yang, Yu Song, Guangrui Qian, Xiong An, Chunhuan Lin, and Guangming Shi. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate the workflow of machine learning. On the PMLB dataset, the proposed approach shows the state-of-the-art performance compared with TPOT, Autostacker, and auto-sklearn. Some of the optimized models are with complex structures which are difficult to obtain in manual design.
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Chau, Duen H. Data Mining Meets HCI: Making Sense of Large Graphs. Fort Belvoir, VA: Defense Technical Information Center, July 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. Fort Belvoir, VA: Defense Technical Information Center, September 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. Fort Belvoir, VA: Defense Technical Information Center, August 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. Fort Belvoir, VA: Defense Technical Information Center, January 2013. http://dx.doi.org/10.21236/ada580208.

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Toroczkai, Zoltan. DARPA Ensemble-Based Modeling Large Graphs & Applications to Social Networks. Fort Belvoir, VA: Defense Technical Information Center, July 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), September 2013. http://dx.doi.org/10.2172/1093898.

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Or, Etti, David Galbraith, and Anne Fennell. Exploring mechanisms involved in grape bud dormancy: Large-scale analysis of expression reprogramming following controlled dormancy induction and dormancy release. United States Department of Agriculture, December 2002. http://dx.doi.org/10.32747/2002.7587232.bard.

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The timing of dormancy induction and release is very important to the economic production of table grape. Advances in manipulation of dormancy induction and dormancy release are dependent on the establishment of a comprehensive understanding of biological mechanisms involved in bud dormancy. To gain insight into these mechanisms we initiated the research that had two main objectives: A. Analyzing the expression profiles of large subsets of genes, following controlled dormancy induction and dormancy release, and assessing the role of known metabolic pathways, known regulatory genes and novel sequences involved in these processes B. Comparing expression profiles following the perception of various artificial as well as natural signals known to induce dormancy release, and searching for gene showing similar expression patterns, as candidates for further study of pathways having potential to play a central role in dormancy release. We first created targeted EST collections from V. vinifera and V. riparia mature buds. Clones were randomly selected from cDNA libraries prepared following controlled dormancy release and controlled dormancy induction and from respective controls. The entire collection (7920 vinifera and 1194 riparia clones) was sequenced and subjected to bioinformatics analysis, including clustering, annotations and GO classifications. PCR products from the entire collection were used for printing of cDNA microarrays. Bud tissue in general, and the dormant bud in particular, are under-represented within the grape EST database. Accordingly, 59% of the our vinifera EST collection, composed of 5516 unigenes, are not included within the current Vitis TIGR collection and about 22% of these transcripts bear no resemblance to any known plant transcript, corroborating the current need for our targeted EST collection and the bud specific cDNA array. Analysis of the V. riparia sequences yielded 814 unigenes, of which 140 are unique (keilin et al., manuscript, Appendix B). Results from computational expression profiling of the vinifera collection suggest that oxidative stress, calcium signaling, intracellular vesicle trafficking and anaerobic mode of carbohydrate metabolism play a role in the regulation and execution of grape-bud dormancy release. A comprehensive analysis confirmed the induction of transcription from several calcium–signaling related genes following HC treatment, and detected an inhibiting effect of calcium channel blocker and calcium chelator on HC-induced and chilling-induced bud break. It also detected the existence of HC-induced and calcium dependent protein phosphorylation activity. These data suggest, for the first time, that calcium signaling is involved in the mechanism of dormancy release (Pang et al., in preparation). We compared the effects of heat shock (HS) to those detected in buds following HC application and found that HS lead to earlier and higher bud break. We also demonstrated similar temporary reduction in catalase expression and temporary induction of ascorbate peroxidase, glutathione reductase, thioredoxin and glutathione S transferase expression following both treatments. These findings further support the assumption that temporary oxidative stress is part of the mechanism leading to bud break. The temporary induction of sucrose syntase, pyruvate decarboxylase and alcohol dehydrogenase indicate that temporary respiratory stress is developed and suggest that mitochondrial function may be of central importance for that mechanism. These finding, suggesting triggering of identical mechanisms by HS and HC, justified the comparison of expression profiles of HC and HS treated buds, as a tool for the identification of pathways with a central role in dormancy release (Halaly et al., in preparation). RNA samples from buds treated with HS, HC and water were hybridized with the cDNA arrays in an interconnected loop design. Differentially expressed genes from the were selected using R-language package from Bioconductor project called LIMMA and clones showing a significant change following both HS and HC treatments, compared to control, were selected for further analysis. A total of 1541 clones show significant induction, of which 37% have no hit or unknown function and the rest represent 661 genes with identified function. Similarly, out of 1452 clones showing significant reduction, only 53% of the clones have identified function and they represent 573 genes. The 661 induced genes are involved in 445 different molecular functions. About 90% of those functions were classified to 20 categories based on careful survey of the literature. Among other things, it appears that carbohydrate metabolism and mitochondrial function may be of central importance in the mechanism of dormancy release and studies in this direction are ongoing. Analysis of the reduced function is ongoing (Appendix A). A second set of hybridizations was carried out with RNA samples from buds exposed to short photoperiod, leading to induction of bud dormancy, and long photoperiod treatment, as control. Analysis indicated that 42 genes were significant difference between LD and SD and 11 of these were unique.
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