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Spectral Algorithms for Streaming Graph Analysis: A Survey

Author

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  • Braznev Sarkar

    (Indian Institute of Engineering Science and Technology)

  • Malay Bhattacharyya

    (Centre for Artificial Intelligence and Machine Learning, Indian Statistical Institute
    Machine Intelligence Unit, Indian Statistical Institute)

Abstract

Streaming data models refer to some constrained settings through which continuous flow of information regarding updates on the data becomes available. Graphs can also be represented in a streaming setting where interaction information turns out to be accessible as a stream of inclusion or exclusion of interactions. Analysis of streaming graphs helps to understand extreme-scale and dynamic real-life interactions in different forms. The growth of world wide web has drastically changed the way we look at various real-life evolving gigantic networks. This has motivated the development of streaming algorithms to be applied on graphs at scale. To achieve this scalability, sketching and sampling strategies are generally adopted to realize the different attributes of graphs. Spectrum of a graph, being one of the most appreciated characteristics, has lead to the evolution of an entire class of spectral algorithms. In this paper, we touch upon the state-of-the-art progress in streaming graph analysis with spectral algorithms. We mainly cover the latest developments in the areas like sampling, sparsification, singular value decomposition, counting problems related to local structures, analysis of global structures, partitioning, labeling, mesh processing, discovery of patterns, anomalous hotspot discovery, detection of communities, etc. on the subject of streaming graphs.

Suggested Citation

  • Braznev Sarkar & Malay Bhattacharyya, 2021. "Spectral Algorithms for Streaming Graph Analysis: A Survey," Annals of Data Science, Springer, vol. 8(4), pages 667-681, December.
    • Handle: RePEc:spr:aodasc:v:8:y:2021:i:4:d:10.1007_s40745-020-00301-0
    DOI: 10.1007/s40745-020-00301-0
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    References listed on IDEAS

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    1. Yong Shi & Zhiguang Shan & Jianping Li & Yufei Fang, 2017. "How China Deals with Big Data," Annals of Data Science, Springer, vol. 4(4), pages 433-440, December.
    2. Devansh Patel & Dhwanil Shah & Manan Shah, 2020. "The Intertwine of Brain and Body: A Quantitative Analysis on How Big Data Influences the System of Sports," Annals of Data Science, Springer, vol. 7(1), pages 1-16, March.
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    Cited by:

    1. Ali Najafi & Araz Gholipour-Shilabin & Rahim Dehkharghani & Ali Mohammadpur-Fard & Meysam Asgari-Chenaghlu, 2023. "ComStreamClust: a Communicative Multi-Agent Approach to Text Clustering in Streaming Data," Annals of Data Science, Springer, vol. 10(6), pages 1583-1605, December.

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