Scalable dynamic graph summarization

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

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

1 Citation (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

Fingerprint

Communication

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

Scalable dynamic graph summarization. / Tsalouchidou, Ioanna; Morales, Gianmarco; Bonchi, Francesco; Baeza-Yates, Ricardo.

Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1032-1039 7840704.

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

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., 7840704, Institute of Electrical and Electronics Engineers Inc., pp. 1032-1039, 4th IEEE International Conference on Big Data, Big Data 2016, Washington, United States, 5/12/16. https://doi.org/10.1109/BigData.2016.7840704
Tsalouchidou I, Morales G, Bonchi F, Baeza-Yates R. Scalable dynamic graph summarization. In Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1032-1039. 7840704 https://doi.org/10.1109/BigData.2016.7840704
Tsalouchidou, Ioanna ; Morales, Gianmarco ; Bonchi, Francesco ; Baeza-Yates, Ricardo. / Scalable dynamic graph summarization. Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1032-1039
@inproceedings{9df81337567940dfb3493806298e2911,
title = "Scalable dynamic graph summarization",
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.",
author = "Ioanna Tsalouchidou and Gianmarco Morales and Francesco Bonchi and Ricardo Baeza-Yates",
year = "2017",
month = "2",
day = "2",
doi = "10.1109/BigData.2016.7840704",
language = "English",
pages = "1032--1039",
booktitle = "Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Scalable dynamic graph summarization

AU - Tsalouchidou, Ioanna

AU - Morales, Gianmarco

AU - Bonchi, Francesco

AU - Baeza-Yates, Ricardo

PY - 2017/2/2

Y1 - 2017/2/2

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85015240348&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85015240348&partnerID=8YFLogxK

U2 - 10.1109/BigData.2016.7840704

DO - 10.1109/BigData.2016.7840704

M3 - Conference contribution

SP - 1032

EP - 1039

BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -