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A Parallel Algorithm for Tracking Dynamic Communities based on Apache Flink

Published: 09 July 2018 Publication History

Abstract

Real world social networks are highly dynamic environments consisting of numerous users and communities, rendering the tracking of their evolution a challenging problem. In this work, we propose a parallel algorithm for tracking dynamic communities between consecutive timeframes of the social network, where communities are represented as undirected graphs. Our method compares the communities based on the widely adopted Jaccard similarity measure and is implemented on top of Apache Flink, a novel framework for parallel and distributed data processing. We evaluate the benefits, in terms of execution time, that parallel processing brings to community tracking on datasets carrying different quantitative characteristics, derived from two popular social media platforms; Twitter and Mathematics Stack Exchange Q&A. Experiments show that our parallel method has the ability to calculate the similarity of communities within seconds, even for large social networks, consisting of more than 600 communities per timeframe.

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Cited By

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  • (2022)A Dynamic Pyramid Tilling Method for Traffic Data Stream Based on FlinkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.306057623:7(6679-6688)Online publication date: Jul-2022
  • (2019)Parallel Heuristic Community Detection Method Based on Node SimilarityIEEE Access10.1109/ACCESS.2019.29605747(184145-184159)Online publication date: 2019

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cover image ACM Other conferences
SETN '18: Proceedings of the 10th Hellenic Conference on Artificial Intelligence
July 2018
339 pages
ISBN:9781450364331
DOI:10.1145/3200947
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

In-Cooperation

  • EETN: Hellenic Artificial Intelligence Society
  • UOP: University of Patras
  • University of Thessaly: University of Thessaly, Volos, Greece

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 July 2018

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Author Tags

  1. Apache Flink
  2. Community Tracking
  3. Parallel Processing
  4. Social Network Analysis

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  • Refereed limited

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SETN '18

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Cited By

View all
  • (2022)A Dynamic Pyramid Tilling Method for Traffic Data Stream Based on FlinkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2021.306057623:7(6679-6688)Online publication date: Jul-2022
  • (2019)Parallel Heuristic Community Detection Method Based on Node SimilarityIEEE Access10.1109/ACCESS.2019.29605747(184145-184159)Online publication date: 2019

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