Scalable dynamic graph summarization

Ioanna Tsalouchidou, Gianmarco Morales, Francesco Bonchi, Ricardo Baeza-Yates

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Large-scale dynamic graphs can be challenging to process and store, due to their size and the continuous change of communication patterns between nodes. In this work we address the problem of summarizing large-scale dynamic graphs, maintaining the evolution of their structure and the communication patterns. Our approach is based on grouping the nodes of the graph in supernodes according to their connectivity and communication patterns. The resulting summary graph preserves the information about the evolution of the graph within a time window. We propose two online, distributed, and tunable algorithms for summarizing this type of graphs. We apply our methods to several real-world and synthetic dynamic graphs, and we show that they scale well on the number of nodes and produce high-quality summaries.

Original languageEnglish
Title of host publicationProceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1032-1039
Number of pages8
ISBN (Electronic)9781467390040
DOIs
Publication statusPublished - 2 Feb 2017
Event4th IEEE International Conference on Big Data, Big Data 2016 - Washington, United States
Duration: 5 Dec 20168 Dec 2016

Other

Other4th IEEE International Conference on Big Data, Big Data 2016
CountryUnited States
CityWashington
Period5/12/168/12/16

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Hardware and Architecture

Cite this

Tsalouchidou, I., Morales, G., Bonchi, F., & Baeza-Yates, R. (2017). Scalable dynamic graph summarization. In Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 (pp. 1032-1039). [7840704] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2016.7840704