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FlexGraph: Flexible partitioning and storage for scalable graph mining.

Chiwan Park1, Ha-Myung Park1, U Kang1

  • 1Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.

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Summary
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FlexGraph analyzes large graphs by reducing communication and I/O costs. This scalable distributed graph mining method processes significantly larger graphs than existing systems.

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Area of Science:

  • Computer Science
  • Data Mining
  • Distributed Systems

Background:

  • Analyzing massive graphs (e.g., the Web, social networks) faces challenges due to high communication and I/O costs in distributed systems.
  • Existing graph mining systems struggle with Web-scale graphs because of repeated subgraph reads and inter-worker communication.

Purpose of the Study:

  • To propose FlexGraph, a novel scalable distributed graph mining method.
  • To reduce communication and I/O costs for processing large-scale graphs.

Main Methods:

  • FlexGraph employs edge placement policies tailored to vertex types to minimize communication overhead.
  • A flexible storage format is introduced to decrease I/O costs associated with repeated graph data access.

Main Results:

  • FlexGraph successfully processed graphs up to 64 times larger than existing distributed memory-based methods.
  • The proposed method consistently outperformed previous disk-based graph mining approaches.

Conclusions:

  • FlexGraph offers a scalable and efficient solution for analyzing massive real-world graphs.
  • The system effectively addresses the communication and I/O bottlenecks inherent in distributed graph mining.