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Efficient Parallel Graph Extraction

  • Yingxia Shao
  • Bin Cui
  • Lei Chen
Chapter
  • 9 Downloads
Part of the Big Data Management book series (BIGDM)

Abstract

In this chapter, we introduce the homogeneous graph extraction task, which extracts homogeneous graphs from the heterogeneous graphs. In an extracted homogeneous graph, the relation is defined by a line pattern on the heterogeneous graph and the new attribute values of the relation are calculated by user-defined aggregate functions. When facing large-scale heterogeneous graphs, the key challenges of the extraction problem are how to efficiently enumerate paths matched by the line pattern and aggregate values for each pair of vertices from the matched paths. To address the above two challenges, we propose a parallel graph extraction framework. The framework compiles the line pattern into a path concatenation plan, which is selected by a cost model. To guarantee the performance of computing aggregate functions, we first classify the aggregate functions into distributive aggregation, algebraic aggregation, and holistic aggregation; then we speed up the distributive and algebraic aggregations by computing partial aggregate values during the path enumeration. The experimental results demonstrate the effectiveness of the proposed graph extraction.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Computer ScienceBeijing University of Posts and Telecommunications BeijingBeijingChina
  2. 2.School of Electronics Engineering and Computer SciencePeking University BeijingBeijingChina
  3. 3.Department of Computer Science and EngineeringHong Kong University of Science and TechnologyHong KongChina

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