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SNAP: A General Purpose Network Analysis and Graph Mining Library.

Jure Leskovec1, Rok Sosič1

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Summary
This summary is machine-generated.

Stanford Network Analysis Platform (SNAP) offers high-performance tools for analyzing large, dynamic networks. This open-source system efficiently processes massive graphs with over 140 algorithms, aiding complex systems research.

Keywords:
Computing platformsData MiningData miningGraph AnalyticsGraphsInformation systems → Data management systemsMain memory enginesNetworksOpen-Source Software

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Area of Science:

  • Computational science
  • Network science
  • Data science

Background:

  • Large networks are crucial for studying complex systems across diverse fields like social network analysis, molecular biology, and neuroscience.
  • Existing tools for analyzing and manipulating large-scale networks are limited, creating a need for efficient solutions.

Purpose of the Study:

  • To introduce the Stanford Network Analysis Platform (SNAP), a high-performance system for the analysis and manipulation of large networks.
  • To detail SNAP's functionality, implementation, and performance benchmarks.
  • To highlight SNAP's capability in handling dynamic graphs and massive datasets.

Main Methods:

  • SNAP is designed for single big-memory machines, optimizing performance and memory usage.
  • It provides a compact in-memory graph representation suitable for dynamic graphs.
  • The system supports hundreds of millions of nodes and billions of edges, offering over 140 graph algorithms.

Main Results:

  • SNAP efficiently processes massive networks, balancing performance, memory representation, and dynamic graph capabilities.
  • It enables manipulation of large graphs, calculation of structural properties, and generation of various graph types.
  • Networks and their attributes are fully dynamic, allowing modification during computation with low overhead.

Conclusions:

  • SNAP is a versatile, open-source C++ library and Python module for large-scale network analysis.
  • It addresses the limitations of existing tools by providing efficient, high-level operations for complex network data.
  • The accompanying Stanford Large Network Dataset offers valuable resources for algorithm development and benchmarking.