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Influence Maximization for Fixed Heterogeneous Thresholds.

P D Karampourniotis1,2, B K Szymanski3,4,5, G Korniss6,3

  • 1Department of Physics, Applied Physics, and Astronomy, Rensselaer Polytechnic Institute, 110 8th Street, Troy, NY, 12180-3590, USA. karamp.pan@gmail.com.

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We introduce two novel metrics, Balanced Index (BI) and Group Performance Index (GPI), to solve the NP-hard influence maximization problem. These metrics efficiently identify key influencers in networks, improving cascade prediction and strategy.

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Area of Science:

  • Network Science
  • Computer Science
  • Data Science

Background:

  • Influence maximization is crucial for targeted information diffusion in networks.
  • The problem is computationally challenging (NP-hard), requiring efficient solutions.
  • Existing methods may not capture diverse influencer characteristics effectively.

Purpose of the Study:

  • To propose and evaluate two novel metrics for influence maximization: Balanced Index (BI) and Group Performance Index (GPI).
  • To assess the performance of BI and GPI under the Linear Threshold Model.
  • To investigate the influence of network structure (degree assortativity) and threshold distributions on cascade size.

Main Methods:

  • Developed the Balanced Index (BI) by combining node degree, susceptibility, and neighborhood impact.
  • Developed the Group Performance Index (GPI) evaluating initiator performance within random sets.
  • Tested both metrics on the Linear Threshold Model with fixed, known thresholds.
  • Analyzed the effect of degree assortativity and threshold distributions on cascade spread.

Main Results:

  • The Balanced Index (BI) efficiently identifies influencers with high resistance and out-degree.
  • The Group Performance Index (GPI) effectively measures initiator performance in group settings.
  • Both BI and GPI demonstrated strong performance in influence maximization tasks.
  • Network degree assortativity and threshold distributions significantly impact cascade size.

Conclusions:

  • The proposed BI and GPI metrics offer effective and computationally efficient approaches to influence maximization.
  • These metrics provide valuable tools for understanding and optimizing information diffusion in complex networks.
  • Further research can explore these metrics across various cascade models and network types.