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iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression.

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This study introduces a novel graph compression method using variable-shape regions to improve compression ratios for large, sparse graphs. The new approach significantly enhances efficiency compared to current state-of-the-art techniques.

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Area of Science:

  • Computer Science
  • Data Science
  • Graph Theory

Background:

  • Real-world graphs, such as social networks, are massive and computationally intensive.
  • Existing graph compression methods struggle with sparse graphs, leading to suboptimal compression ratios.
  • Current state-of-the-art (SOTA) approaches decompose graphs into fixed-size submatrices, which is inefficient for power-law distributed graphs.

Purpose of the Study:

  • To develop an advanced ordered matrix compression technique for large-scale graphs.
  • To improve graph compression ratios by addressing the limitations of fixed-size submatrix decomposition.
  • To enhance computational and memory efficiency in processing massive graph data.

Main Methods:

  • The proposed method divides graph matrices into variable-shape sub-blocks, rather than fixed-size ones.
  • It considers both horizontal and vertical shaped regions for optimized compression.
  • This deep-level division aims to minimize empty cell processing and maximize compression efficiency.

Main Results:

  • The novel approach achieved an average compression ratio of 93.8%.
  • This represents a significant improvement over existing SOTA graph compression techniques.
  • Empirical evaluations demonstrate the effectiveness of variable-shape region compression for sparse graphs.

Conclusions:

  • Variable-shape region decomposition is a superior strategy for ordered matrix compression in large graphs.
  • The proposed method offers substantial improvements in compression ratio and efficiency for real-world graph data.
  • This research provides a more effective solution for handling the computational challenges of massive graph processing.