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Relevant bibliographies by topics / Distributed Graph Processing

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Academic literature on the topic 'Distributed Graph Processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 2 February 2022

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Journal articles on the topic "Distributed Graph Processing"

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Onizuka, Makoto, Toshimasa Fujimori, and Hiroaki Shiokawa. "Graph Partitioning for Distributed Graph Processing." Data Science and Engineering 2, no. 1 (2017): 94–105. http://dx.doi.org/10.1007/s41019-017-0034-4.

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Zhuo, Youwei, Jingji Chen, Gengyu Rao, et al. "Distributed Graph Processing System and Processing-in-memory Architecture with Precise Loop-carried Dependency Guarantee." ACM Transactions on Computer Systems 37, no. 1-4 (2021): 1–37. http://dx.doi.org/10.1145/3453681.

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To hide the complexity of the underlying system, graph processing frameworks ask programmers to specify graph computations in user-defined functions (UDFs) of graph-oriented programming model. Due to the nature of distributed execution, current frameworks cannot precisely enforce the semantics of UDFs, leading to unnecessary computation and communication. It exemplifies a gap between programming model and runtime execution. This article proposes novel graph processing frameworks for distributed system and Processing-in-memory (PIM) architecture that precisely enforces loop-carried dependency;

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Liakos, Panagiotis, Katia Papakonstantinopoulou, and Alex Delis. "Realizing Memory-Optimized Distributed Graph Processing." IEEE Transactions on Knowledge and Data Engineering 30, no. 4 (2018): 743–56. http://dx.doi.org/10.1109/tkde.2017.2779797.

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De Werra, D., P. Hell, T. Kameda, N. Katoh, Ph Solot, and M. Yamashita. "Graph endpoint coloring and distributed processing." Networks 23, no. 2 (1993): 93–98. http://dx.doi.org/10.1002/net.3230230203.

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Yan, Da, Yuzhen Huang, Miao Liu, et al. "GraphD: Distributed Vertex-Centric Graph Processing Beyond the Memory Limit." IEEE Transactions on Parallel and Distributed Systems 29, no. 1 (2018): 99–114. http://dx.doi.org/10.1109/tpds.2017.2743708.

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Danilov, I. G., and S. I. Rodzin. "Distributed implementation of the graphs clusterization goodness metrics calculation using mapreduce and vertex-oriented graph processing model." Informatization and communication, no. 3 (May 5, 2020): 31–35. http://dx.doi.org/10.34219/2078-8320-2020-11-3-31-35.

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Goal. Research of the implementation features of the graph clusterization goodness metrics using the vertexoriented graph computing model and MapReduce. Materials and methods. The basic concepts of graph theory were used to define the goodness metrics, and the MapReduce with a vertex-oriented graph-computational approach were used to develop the goodness metrics calculation algorithms. Results. Distributed algorithms for calculating graph clustering goodness metrics are proposed and tested. Conclusion. The results can be used to analyze the quality of the partitioning of large graphs that obta

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Bouhenni, Sarra, Saïd Yahiaoui, Nadia Nouali-Taboudjemat, and Hamamache Kheddouci. "A Survey on Distributed Graph Pattern Matching in Massive Graphs." ACM Computing Surveys 54, no. 2 (2021): 1–35. http://dx.doi.org/10.1145/3439724.

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Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distri

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Dhulipala, Laxman, Guy E. Blelloch, and Julian Shun. "Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable." ACM Transactions on Parallel Computing 8, no. 1 (2021): 1–70. http://dx.doi.org/10.1145/3434393.

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There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Ther

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Shen, Yanyan, Gang Chen, H. V. Jagadish, Wei Lu, Beng Chin Ooi, and Bogdan Marius Tudor. "Fast failure recovery in distributed graph processing systems." Proceedings of the VLDB Endowment 8, no. 4 (2014): 437–48. http://dx.doi.org/10.14778/2735496.2735506.

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Kalavri, Vasiliki, Vladimir Vlassov, and Seif Haridi. "High-Level Programming Abstractions for Distributed Graph Processing." IEEE Transactions on Knowledge and Data Engineering 30, no. 2 (2018): 305–24. http://dx.doi.org/10.1109/tkde.2017.2762294.

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Dissertations / Theses on the topic "Distributed Graph Processing"

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Kumar, Rohit 1986. "Temporal graph mining and distributed processing." Doctoral thesis, Universitat Politècnica de Catalunya, 2018. http://hdl.handle.net/10803/620623.

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With the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamic

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Kumar, Rohit. "Temporal Graph Mining and Distributed Processing." Doctoral thesis, Universite Libre de Bruxelles, 2018. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/271527.

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With the recent growth of social media platforms and the human desire to interact with the digital world a lot of human-human and human-device interaction data is getting generated every second. With the boom of the Internet of Things (IoT) devices, a lot of device-device interactions are also now on the rise. All these interactions are nothing but a representation of how the underlying network is connecting different entities over time. These interactions when modeled as an interaction network presents a lot of unique opportunities to uncover interesting patterns and to understand the dynamic

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Mayer, Christian [Verfasser], and Kurt [Akademischer Betreuer] Rothermel. "Scalable graph partitioning for distributed graph processing / Christian Mayer ; Betreuer: Kurt Rothermel." Stuttgart : Universitätsbibliothek der Universität Stuttgart, 2019. http://d-nb.info/1196095353/34.

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Collet, Julien. "Exploration of parallel graph-processing algorithms on distributed architectures." Thesis, Compiègne, 2017. http://www.theses.fr/2017COMP2391/document.

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Avec l'explosion du volume de données produites chaque année, les applications du domaine du traitement de graphes ont de plus en plus besoin d'être parallélisées et déployées sur des architectures distribuées afin d'adresser le besoin en mémoire et en ressource de calcul. Si de telles architectures larges échelles existent, issue notamment du domaine du calcul haute performance (HPC), la complexité de programmation et de déploiement d’algorithmes de traitement de graphes sur de telles cibles est souvent un frein à leur utilisation. De plus, la difficile compréhension, a priori, du comportemen

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Kalavri, Vasiliki. "Performance Optimization Techniques and Tools for Distributed Graph Processing." Doctoral thesis, KTH, Programvaruteknik och Datorsystem, SCS, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-192471.

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In this thesis, we propose optimization techniques for distributed graph processing. First, we describe a data processing pipeline that leverages an iterative graph algorithm for automatic classification of web trackers. Using this application as a motivating example, we examine how asymmetrical convergence of iterative graph algorithms can be used to reduce the amount of computation and communication in large-scale graph analysis. We propose an optimization framework for fixpoint algorithms and a declarative API for writing fixpoint applications. Our framework uses a cost model to automatical

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Abdlwafa, Alan, and Henrik Edman. "Distributed Graph Mining : A study of performance advantages in distributed data mining paradigms when processing graphs using PageRank on a single node cluster." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-166449.

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Distributed data mining is a relatively new area within computer science that is steadily growing, emerging from the demands of being able to gather and process various distributed data by utilising clusters. This report presents the properties of graph structured data and what paradigms to use for efficiently processing the data type, based on comprehensive theoretical studies applied on practical tests performed on a single node cluster. The results in the study showcase the various performance aspects of processing graph data, using different open source paradigm frameworks and amount of sh

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Gadde, Srimanth. "Graph Partitioning Algorithms for Minimizing Inter-node Communication on a Distributed System." University of Toledo / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1376561814.

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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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Kruzick, Stephen M. "Optimal Graph Filter Design for Large-Scale Random Networks." Research Showcase @ CMU, 2018. http://repository.cmu.edu/dissertations/1165.

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Graph signal processing analyzes signals supported on the nodes of a network with respect to a shift operator matrix that conforms to the graph structure. For shift-invariant graph filters, which are polynomial functions of the shift matrix, the filter response is defined by the value of the filter polynomial at the shift matrix eigenvalues. Thus, information regarding the spectral decomposition of the shift matrix plays an important role in filter design. However, under stochastic conditions leading to uncertain network structure, the eigenvalues of the shift matrix become random, complicatin

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Cui, Beibei. "Image processing applications in object detection and graph matching : from Matlab development to GPU framework." Thesis, Bourgogne Franche-Comté, 2020. http://www.theses.fr/2020UBFCA002.

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Déterminer des mises en correspondance d’objet, ou de caractéristiques d’objet, dans des images présente un grand intérêt pour beaucoup d’applications telles que la détection et le suivi de cible, l’estimation du flot optique, l’identification, et d’autres tâches dérivées. Dans cette thèse, nous abordons le problème de mise en correspondance dans le cadre général de l’optimisation de l’appariement de graphe, dans le but de contribuer, comme résultat final, au développement de nouveaux algorithmes parallèles implémentés sur plateforme GPU (Graphics Processing Unit). Le problème d’appariement de

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Books on the topic "Distributed Graph Processing"

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International, Workshop on Distributed Algorithms (1st 1985 Ottawa Ont ). Distributed algorithms on graphs: Proceedings of the 1st International Workshop on Distributed Algorithms, Ottawa, Canada, August 1985. Carleton University Press, 1986.

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(Ottawa, Ont ). International Workshop on Distributed Algorithms 1985. Distributed Algorithms on Graphs: Proceedings of the 1st International Workshop on Distributed Algorithms, Ottawa, Canada, August 1985. Carleton Univ Pr, 1987.

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-C, Bermond J., Raynal M, and International Workshop on Distributed Algorithms (3rd : 1989 : La Colle-sur-Loup, France), eds. Distributed algorithms: 3rd international workshop, Nice, France, September 26-28, 1989 : proceedings. Springer-Verlag, 1989.

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Book chapters on the topic "Distributed Graph Processing"

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Zhang, Yanfeng, Qiange Wang, and Shufeng Gong. "Distributed Graph Processing: Techniques and Systems." In Communications in Computer and Information Science. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0479-9_2.

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Chakaravarthy, Venkatesan T., Aashish Goyal, Prakash Murali, Shivmaran S. Pandian, and Yogish Sabharwal. "Improved Distributed Algorithm for Graph Truss Decomposition." In Euro-Par 2018: Parallel Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96983-1_50.

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Hamann, Michael, Ben Strasser, Dorothea Wagner, and Tim Zeitz. "Distributed Graph Clustering Using Modularity and Map Equation." In Euro-Par 2018: Parallel Processing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96983-1_49.

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Kosowski, Adrian, and Łukasz Kuszner. "On Greedy Graph Coloring in the Distributed Model." In Euro-Par 2006 Parallel Processing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11823285_61.

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Upadhyay, Nitesh, Parita Patel, Unnikrishnan Cheramangalath, and Y. N. Srikant. "Large Scale Graph Processing in a Distributed Environment." In Euro-Par 2017: Parallel Processing Workshops. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75178-8_38.

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Zhang, Yu, Yanbing Liu, Jing Yu, Ping Liu, and Li Guo. "VSEP: A Distributed Algorithm for Graph Edge Partitioning." In Algorithms and Architectures for Parallel Processing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-27161-3_7.

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Angskun, Thara, George Bosilca, and Jack Dongarra. "Binomial Graph: A Scalable and Fault-Tolerant Logical Network Topology." In Parallel and Distributed Processing and Applications. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74742-0_43.

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Effantin, Brice, and Hamamache Kheddouci. "A Distributed Algorithm for a b-Coloring of a Graph." In Parallel and Distributed Processing and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11946441_42.

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Tajozzakerin, Hamid Reza, and Hamid Sarbazi-Azad. "Enhanced-Star: A New Topology Based on the Star Graph." In Parallel and Distributed Processing and Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30566-8_117.

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Grabska, Ewa, and Barbara Strug. "Applying Cooperating Distributed Graph Grammars in Computer Aided Design." In Parallel Processing and Applied Mathematics. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11752578_68.

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Conference papers on the topic "Distributed Graph Processing"

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Carlini, Emanuele, Patrizio Dazzi, Alessandro Lulli, and Laura Ricci. "Distributed graph processing." In SAC 2016: Symposium on Applied Computing. ACM, 2016. http://dx.doi.org/10.1145/2851613.2851746.

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Chen, Tefeng, and Bo Li. "A Distributed Graph Partitioning Algorithm for Processing Large Graphs." In 2016 IEEE Symposium on Service-Oriented System Engineering (SOSE). IEEE, 2016. http://dx.doi.org/10.1109/sose.2016.48.

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Augustine, John, Keerti Choudhary, Avi Cohen, David Peleg, Sumathi Sivasubramaniam, and Suman Sourav. "Distributed Graph Realizations †." In 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS). IEEE, 2020. http://dx.doi.org/10.1109/ipdps47924.2020.00026.

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Delling, Daniel, Andrew V. Goldberg, Ilya Razenshteyn, and Renato F. Werneck. "Graph Partitioning with Natural Cuts." In Distributed Processing Symposium (IPDPS). IEEE, 2011. http://dx.doi.org/10.1109/ipdps.2011.108.

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Li-Yung Ho, Jan-Jan Wu, and Pangfeng Liu. "Data Replication for Distributed Graph Processing." In 2013 IEEE 6th International Conference on Cloud Computing (CLOUD). IEEE, 2013. http://dx.doi.org/10.1109/cloud.2013.55.

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Su, Jing, Qun Chen, Zhuo Wang, Murtadha Ahmed, and Zhanhuai Li. "GraphU: A Unified Vertex-Centric Parallel Graph Processing Platform." In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). IEEE, 2018. http://dx.doi.org/10.1109/icdcs.2018.00160.

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Asahiro, Yuichi, Jesper Jansson, Eiji Miyano, and Hirotaka Ono. "Graph orientation to maximize the minimum weighted outdegree." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5160872.

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Gallet, Matthieu, Loris Marchal, and Frederic Vivien. "Efficient scheduling of task graph collections on heterogeneous resources." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5161045.

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Hirani, Anil N., Kaushik Kalyanaraman, and Seth Watts. "Graph Laplacians and Least Squares on Graphs." In 2015 IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPSW). IEEE, 2015. http://dx.doi.org/10.1109/ipdpsw.2015.73.

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Toader, Lucian, Alexandru Uta, Ahmed Musaafir, and Alexandru Iosup. "Graphless: Toward Serverless Graph Processing." In 2019 18th International Symposium on Parallel and Distributed Computing (ISPDC). IEEE, 2019. http://dx.doi.org/10.1109/ispdc.2019.00012.

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Reports on the topic "Distributed Graph Processing"

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Chaparro, Rodrigo, Maria Netto, Patricio Mansilla, and Daniel Magallon. Energy Savings Insurance: Advances and Opportunities for Funding Small- and Medium-Sized Energy Efficiency and Distributed Generation Projects in Chile. Inter-American Development Bank, 2020. http://dx.doi.org/10.18235/0002947.

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The Energy Savings Insurance Program seeks to promote investment in energy efficiency and distributed generation in Latin America, primarily through small- and medium-sized enterprises (SMEs). It focuses on developing an innovative scheme of guaranteed energy performance that mitigates project risk and generates investor confidence (ESI Model). The Inter-American Development Bank (IDB) facilitates the development of the ESI Program in alliance with the National Development Banks (NDBs). The ESI Model includes a contract for the supply, installation, and maintenance of equipment for generating

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