Synonyms
Glossary
- BSP :
-
Bulk Synchronous Parallel
- MapReduce :
-
A distributed programming model derived from functional paradigm, dedicated for complex and distributed computations
- SNA :
-
Social network analysis
Definition
The rapid development of the Internet provides many data sets that can be used to extract large and complex social networks. Such structures are characterized by the 3V rule, typical for big data sets: variety, volume, and velocity. These properties require sophisticated environment and specialized methods to be used for processing and analyzing large social networks. The primary purpose of various techniques, measures, and methods commonly called social network analysis (SNA) is to extract useful knowledge from such structures in order to support, e.g., targeted marketing, recommender, and personalized systems, or efficient human collaboration and knowledge exchange.
Due to efficiency reasons, to process large networked data, some complex cluster...
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References
Abou-Rjeili A, Karypis G (2006) Multilevel algorithms for partitioning power-law graphs. The 20th International Conference on Parallel and Distributed Processing – IPDPS 2006, IEEE, p 124
Andrews GR (2000) Foundations of multithreaded, parallel, and distributed programming. Addison–Wesley, Reading
Apache Giraph (2011) http://giraph.apache.org/. Accessed 7 Apr 2017
Avery C (2011) Giraph: large-scale graph processing infrastructure on hadoop. Proceedings of the Hadoop Summit, Santa Clara
Bartusiak Roman, Tomasz Kajdanowicz (2017) SparklingGraph – Large scale, distributed (not only!) graph processing made easy! http://sparkling.ml/. Accessed 30 Mar 2017
Ching A, Edunov S, Kabiljo M, Logothetis D, Muthukrishnan S (2015) One trillion edges: graph processing at Facebook-scale. Proceedings of the VLDB Endowment, 8(12), 1804–1815
Cohen J (2009) Graph twiddling in a MapReduce world. Comput Sci Eng 11:29–41
Gonzalez JE, Low Y, Gu H, Bickson D, Guestrin C (2012) PowerGraph: distributed graph-parallel computation on natural graphs. In: The 10th USENIX Symposium on Operating systems design and implementation – OSDI ’12, Hollywood, pp 17–30
Indyk W, Kajdanowicz T, Kazienko P, Plamowski S (2012) MapReduce approach to collective classification for networks. In: ICAISC 2012, Zakopane. Lecture notes in computer science, vol 7267. pp 656–663
Kajdanowicz T, Kazienko P, Indyk W (2014b) Parallel processing of large graphs. Futur Gener Comput Syst 32:324–337
Kajdanowicz T, Indyk W, Kazienko P, Kukuł J (2012) Comparison of the efficiency of MapReduce and bulk synchronous parallel approaches to large network processing. In: ICDM 2012 – IEEE international conference on data mining, DaMNet 2012 – the second IEEE ICDM workshop on data mining in networks, Brussels. IEEE Computer Society Press, pp 218–225
Kajdanowicz T, Indyk W, Plamowski S, Kazienko P (2014a) MapReduce approach to relational influence propagation in complex networks. Pattern Anal Applic 17(4):739–746. https://doi.org/10.1007/s10044-012-0294-6
Kim GH, Trimi S, Chung JH (2014) Big-data applications in the government sector. Commun ACM 57(3):78–85
Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123
Lin J, Dyer C (2010) Data-Intensive text processing with MapReduce. Synthesis lectures on human language technologies. Morgan & Claypool Publishers, San Rafael
Lin J, Schatz M (2010) Design patterns for efficient graph algorithms in MapReduce. In: The eighth workshop on mining and learning with graphs – MLG'10. ACM, New York, pp 78–85
Malewicz G, Austern MH, Bik AJ, Dehnert JC, Horn I, Leiser N, Czajkowski G (2010) Pregel: a system for large-scale graph processing. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of data, ACM, pp 135–146
Pace MF (2012) BSP vs MapReduce. Procedia Comput Sci 9:246–255
Valiant L (1990) A bridging model for parallel computation. Commun ACM 33(8):103–111
White T (2010) Hadoop: the definitive guide, 2nd edn. O'Reilly, Sebastopol
Xin RS, Gonzalez JE, Franklin MJ, Stoica I (2013) Graphx: a resilient distributed graph system on spark. The First International Workshop on Graph Data Management Experiences and Systems, ACM, p 2
Acknowledgments
This work was partially supported by the National Science Centre, Poland, the decisions no. DEC-2016/21/B/ST6/01463 and DEC-2016/21/D/ST6/02948.
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Kazienko, P., Indyk, W., Kajdanowicz, T., Bartusiak, R. (2018). Distributed Processing of Networked Data. In: Alhajj, R., Rokne, J. (eds) Encyclopedia of Social Network Analysis and Mining. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-7131-2_258
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