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Locating influential nodes in complex networks.

Fragkiskos D Malliaros1, Maria-Evgenia G Rossi1, Michalis Vazirgiannis1

  • 1Computer Science Laboratory, École Polytechnique, 91120 Palaiseau, France.

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|January 19, 2016
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Summary
This summary is machine-generated.

Identifying influential spreaders in networks is key for efficient information diffusion. This study finds that nodes in maximal K-truss subgraphs exhibit superior spreading behavior, outperforming traditional metrics like node degree.

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Area of Science:

  • Network science
  • Graph theory
  • Complex systems analysis

Background:

  • Controlling spreading processes in networks is vital for applications like disease propagation and viral marketing.
  • Identifying influential nodes is crucial for efficient information diffusion and resource optimization.

Purpose of the Study:

  • To introduce and evaluate the K-truss decomposition for identifying influential nodes in networks.
  • To compare the spreading capabilities of K-truss nodes against existing importance criteria.

Main Methods:

  • Utilizing K-truss decomposition, an extension of graph core decomposition based on triangles.
  • Analyzing node influence on real-world network datasets.
  • Comparing spreading behavior of nodes within maximal K-truss subgraphs with node degree and k-core index.

Main Results:

  • Nodes within maximal K-truss subgraphs demonstrate significantly better spreading behavior.
  • K-truss nodes lead to faster and wider epidemic spreading compared to nodes identified by degree or k-core index.
  • These dense subgraph nodes are critical for achieving optimal network-wide spreading.

Conclusions:

  • K-truss decomposition offers a novel and effective method for identifying influential spreaders.
  • Maximal K-truss subgraph membership is a superior indicator of spreading potential in networks.
  • Targeting nodes in dense subgraphs can optimize network-based spreading processes.