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MATI: An efficient algorithm for influence maximization in social networks.

Maria-Evgenia G Rossi1, Bowen Shi1, Nikolaos Tziortziotis1

  • 1École Polytechnique, Palaiseau, France.

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|November 2, 2018
PubMed
Summary
This summary is machine-generated.

We introduce Matrix Influence (MATI), an efficient algorithm for influence maximization. MATI improves scalability for large networks while maintaining competitive influence spread compared to existing methods.

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Area of Science:

  • Network Science
  • Computer Science
  • Data Science

Background:

  • Influence maximization is crucial for applications like viral marketing and disease spread.
  • The greedy algorithm is effective but struggles with large-scale datasets.
  • Efficient influence maximization is needed for complex network analysis.

Purpose of the Study:

  • To propose Matrix Influence (MATI), an efficient algorithm for influence maximization.
  • To evaluate MATI's performance under Linear Threshold and Independent Cascade models.
  • To demonstrate MATI's scalability and effectiveness on real-world data.

Main Methods:

  • MATI algorithm based on precalculating influence using simple paths.
  • Implementation compatible with Linear Threshold and Independent Cascade diffusion models.
  • Extensive empirical analysis on multiple real-world datasets.

Main Results:

  • MATI demonstrates competitive performance in terms of running time.
  • MATI achieves comparable expected influence spread to established algorithms.
  • The algorithm shows improved scalability for large network analysis.

Conclusions:

  • MATI offers an efficient and scalable solution for influence maximization.
  • The algorithm provides a viable alternative to traditional methods for large datasets.
  • MATI effectively balances computational efficiency with influence spread maximization.