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Effective submodularity of influence maximization on temporal networks.

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Influence maximization on temporal networks is challenging as the influence function is not submodular. However, greedy optimization effectively behaves as submodular, providing near-optimal solutions for influence spread.

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Area of Science:

  • Network Science
  • Computer Science
  • Data Science

Background:

  • Influence maximization is crucial for information diffusion in networks.
  • Temporal networks present unique challenges due to their dynamic nature.
  • Submodularity is a key property for guaranteeing optimality in greedy algorithms.

Purpose of the Study:

  • To investigate the behavior of the influence function in temporal networks.
  • To evaluate the effectiveness of greedy optimization for influence maximization in these dynamic settings.
  • To determine if greedy strategies offer performance guarantees despite non-submodular conditions.

Main Methods:

  • Analysis of influence function properties on real and synthetic temporal networks.
  • Comparison of randomly sampled seed sets versus greedily selected seed sets.
  • Benchmarking greedy optimization against brute-force solutions for accuracy.

Main Results:

  • Randomly sampled seed sets often violate submodularity conditions in temporal networks.
  • Greedy optimization demonstrates near-submodular behavior on both real and synthetic networks.
  • Greedy strategies yield approximate solutions with optimality gaps comparable to strictly submodular functions.

Conclusions:

  • Greedy optimization is a robust and effective strategy for influence maximization on temporal networks.
  • The assumption of submodularity is often practically met by greedy approaches in dynamic networks.
  • This study validates greedy algorithms for practical influence spread maximization in temporal network analysis.