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      • Department of Computer Engineering
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      Cascade-aware partitioning of large graph databases

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      Author(s)
      Demirci, Gündüz Vehbi
      Ferhatosmanoğlu, H.
      Aykanat, Cevdet
      Date
      2019
      Source Title
      The VLDB Journal
      Print ISSN
      1066-8888
      Publisher
      Springer
      Volume
      28
      Issue
      3
      Pages
      329 - 350
      Language
      English
      Type
      Article
      Item Usage Stats
      146
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      130
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      Abstract
      Graph partitioning is an essential task for scalable data management and analysis. The current partitioning methods utilize the structure of the graph, and the query log if available. Some queries performed on the database may trigger further operations. For example, the query workload of a social network application may contain re-sharing operations in the form of cascades. It is beneficial to include the potential cascades in the graph partitioning objectives. In this paper, we introduce the problem of cascade-aware graph partitioning that aims to minimize the overall cost of communication among parts/servers during cascade processes. We develop a randomized solution that estimates the underlying cascades, and use it as an input for partitioning of large-scale graphs. Experiments on 17 real social networks demonstrate the effectiveness of the proposed solution in terms of the partitioning objectives.
      Keywords
      Graph partitioning
      Information cascade
      Propagation models
      Randomized algorithms
      Scalability
      Social networks
      Permalink
      http://hdl.handle.net/11693/52999
      Published Version (Please cite this version)
      https://dx.doi.org/10.1007/s00778-018-0531-8
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      • Department of Computer Engineering 1561
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