Identification of Influential Nodes in Social Network: Big Data - Hadoop
Abstract
Software development and associated data is the most critical factor these days. Currently, people are living in an internet world where data and related artifacts are major sets of information these days. The data is correlated with real-world data. The analysis of large datasets was done as part of the experimental analysis. The dataset for online social media like Facebook and Twitter was taken for the identification of influential nodes. The analysis of the dataset provides an overview and observation of the dataset for Facebook or Twitter. Here, in the current activity, an overview of cloud computing and big data technologies are discussed along with effective methods and approaches to resolve the problem statement. Particularly, big data technologies such as Hadoop provided by Apache for processing and analysis of Gigabyte(GB) or petabyte(PB) scale datasets are discussed for processing data in distributed and parallel data fashion. Here, the processing of large datasets is done by big data technology by implementing Apache Hadoop in online social media.
Article Details
How to Cite
[1]
R. K. Kumar, Laxmi Ahuja, and Suman Mann, “Identification of Influential Nodes in Social Network: Big Data - Hadoop”, Int. J. Data. Science., vol. 5, no. 1, Feb. 2024.
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References
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Apache SparkTM. Unifified Engine for Large-Scale Data Analytics. Available online: https://spark.apache.org/.
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WIN, pp. 1-6, 2011
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Koo, J.; Kang, G.; Kim, Y.G. Security and privacy in big data life cycle: A survey and open challenges. Sustainability 2020, 12, 10571.
Prerna, Agarwal.; Rafeeq, Ahmed.;Tanvir, Ahmad.; Identification and ranking of key persons in a Social Networking Website using Hadoop & Big Data Analytics AICTC '16: Proceedings of the International Conference on Advances in Information Communication Technology & ComputingAugust 2016Article No.: 65Pages 1–6https://doi.org/10.1145/2979779.2979844
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Anderson, J.W.; Kennedy, K.E.; Ngo, L.B.; Luckow, A.; Apon, A.W. Synthetic data generation for the internet of things. In Proceedings of the 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 27–30 October 2014; pp. 171–176.
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Viecco, C. Improving honeynet data analysis. In Proceedings of the 2007 IEEE SMC Information Assurance and Security Workshop, West Point, NY, USA, 20–22 June 2007; pp. 99–106.
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Kojoney—A Honeypot for the SSH Service. Available online: http://kojoney.sourceforge.net/.Information 2023, 14, 93 32 of 34
Honeynet. Capture-HPC. Available online: https://www.honeynet.org/projects/old/capture-hpc/.
Apache Kafka Home Page. Available online: https://kafka.apache.org/.
Padgavankar, M.H.; Gupta, S.R. Big data storage and challenges. Int. J. Comput. Sci. Inf. Technol. 2014, 5, 2218–2223.
Hypertable.org Home Page. Available online: https://hypertable.org/.
Khezr, S.N.; Navimipour, N.J. MapReduce and its applications, challenges, and architecture: A comprehensive review and directions for future research. J. Grid Comput. 2017, 15, 295–321