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Netgram: Visualizing Communities in Evolving Networks.

Raghvendra Mall1, Rocco Langone1, Johan A K Suykens1

  • 1KU Leuven, ESAT-STADIUS, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium.

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Summary
This summary is machine-generated.

Netgram visualizes community evolution in dynamic networks, tracking group changes like birth, death, and merging. This tool aids understanding complex network dynamics and aids in visualizing scientific field evolution.

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Area of Science:

  • Complex networks
  • Network dynamics
  • Data visualization

Background:

  • Real-world complex networks are dynamic, with interactions changing over time.
  • Community structures are key features in complex networks, and their evolution is crucial.
  • Existing methods lack a generic tool to visualize all aspects of group evolution in dynamic networks.

Purpose of the Study:

  • To propose Netgram, a novel tool for visualizing community evolution in time-evolving graphs.
  • To provide a generic toolkit compatible with any evolutionary community detection algorithm.
  • To address the limitations of current methods in visualizing dynamic network structures.

Main Methods:

  • Netgram utilizes tables of community evolution across consecutive time-stamps.
  • A SQL outer-join operation creates a query database for visualization.
  • A line-based visualization technique with a greedy ordering solution minimizes line crossovers for clarity.

Main Results:

  • Netgram successfully visualizes various community evolution aspects: birth, death, splitting, merging, expansion, shrinkage, and continuation.
  • The tool demonstrated effectiveness in visualizing topic evolution in NIPS conference data over 11 years.
  • Emergence and merging of disciplines within information processing systems were observed.

Conclusions:

  • Netgram offers a comprehensive solution for visualizing community evolution in dynamic networks.
  • The tool enhances the understanding of complex network dynamics and evolutionary patterns.
  • Netgram's generic nature allows application across diverse time-evolving graph analysis tasks.