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A parallel algorithm for tracking dynamic communities based on apache flink

Kechagias Georgios, Tzortzis Grigorios, Paliouras, Georgios, Vogiatzis, Dimitrios

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URIhttp://purl.tuc.gr/dl/dias/BFD09074-C229-4CF4-A089-1A4EF0B39690-
Identifierhttps://doi.org/10.1145/3200947.3201039-
Identifierhttps://dl.acm.org/citation.cfm?id=3201039-
Languageen-
Extent4 pagesen
TitleA parallel algorithm for tracking dynamic communities based on apache flinken
CreatorKechagias Georgiosen
CreatorΚεχαγιας Γεωργιοςel
CreatorTzortzis Grigoriosen
CreatorPaliouras, Georgiosen
CreatorVogiatzis, Dimitriosen
PublisherAssociation for Computing Machineryen
Content SummaryReal 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.en
Type of ItemΠλήρης Δημοσίευση σε Συνέδριοel
Type of ItemConference Full Paperen
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2019-08-30-
Date of Publication2018-
SubjectApache Flinken
SubjectCommunity Trackingen
SubjectParallel Processingen
SubjectSocial Network Analysis en
Bibliographic CitationG. Kechagias, G. Tzortzis, G. Paliouras and D. Vogiatzis, "A parallel algorithm for tracking dynamic communities based on apache flink," in 10th Hellenic Conference on Artificial Intelligence, 2018. doi: 10.1145/3200947.3201039en

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