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A graph exploration method for identifying influential spreaders in complex networks.

Nikos Salamanos1, Elli Voudigari1, Emmanuel J Yannakoudakis1

  • 1Department of Informatics, Athens University of Economics and Business, 76, Patission Street, Athens, 10434 Greece.

Applied Network Science
|November 17, 2018
PubMed
Summary
This summary is machine-generated.

Identifying influential spreaders in complex networks is crucial for information diffusion and disease control. A novel graph sampling method accurately finds these key nodes using only partial network data, achieving high accuracy with minimal exploration.

Keywords:
Complex networksGraph miningInfluential spreaders

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Area of Science:

  • Network Science
  • Computational Social Science
  • Epidemiology

Background:

  • Identifying influential spreaders in complex networks is vital for applications like information diffusion, disease control, and network resilience.
  • Existing methods often require complete network knowledge, which is impractical for large, real-world systems.

Purpose of the Study:

  • To propose and evaluate a graph exploration sampling method for accurately identifying influential spreaders in complex networks without prior global graph knowledge.
  • To assess the method's effectiveness using susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) epidemic models on real-world networks.

Main Methods:

  • A novel graph exploration sampling method based on a modified Rank Degree algorithm is introduced.
  • The method explores the network deterministically, collecting samples (edges crossed) without needing the full graph structure.
  • Node influence was assessed using degree centrality and k-core measures on the sampled subgraphs and compared to full graph analysis.

Main Results:

  • The proposed sampling method accurately identifies influential spreaders by exploring only 20% of the network.
  • Accuracy in identifying influential nodes using sampled degree centrality is comparable to using full graph information.
  • Degree centrality calculated from samples shows high accuracy, nearly matching k-core values derived from the complete original graph.

Conclusions:

  • Graph exploration sampling offers an efficient and accurate approach to identifying influential spreaders in large, complex networks.
  • This method significantly reduces the need for complete network data, making influence identification more feasible in practical scenarios.
  • Degree centrality on sampled data is a reliable and effective proxy for identifying influential nodes, even outperforming k-core on the full graph in some aspects.