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Parallel social behavior-based algorithm for identification of influential users in social network.

Wassim Mnasri1, Mehdi Azaouzi1,2, Lotfi Ben Romdhane1

  • 1MARS Research Laboratory LR17ES05, University of Sousse, Sousse, Tunisia.

Applied Intelligence (Dordrecht, Netherlands)
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This study introduces PSAIIM, a parallel algorithm for influence maximization in social networks. It identifies influential users by considering social behavior and user interests, improving calculation speed.

Keywords:
Behavior attributesCPU architectureCommon interestInfluence analysisParallel algorithmSocial networks analysis

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Area of Science:

  • Computer Science
  • Network Analysis
  • Artificial Intelligence

Background:

  • Social networks are growing exponentially, making analysis difficult.
  • Existing influence maximization methods often overlook user social behavior, focusing solely on network structure.
  • Analyzing large-scale social networks requires efficient computational approaches.

Purpose of the Study:

  • To develop a parallel algorithm for influence maximization that incorporates social behavior.
  • To identify influential users more effectively by considering their interests and interactions.
  • To improve the speed and efficiency of influence maximization in large social networks.

Main Methods:

  • Introduced a novel parallel algorithm named PSAIIM.
  • Utilized two semantic metrics: user interests and dynamically-weighted social actions.
  • Employed community structure for perfect parallelism on CPU architecture to optimize influential node computation.

Main Results:

  • The PSAIIM algorithm demonstrated effectiveness in identifying influential users.
  • Achieved significant improvements in calculation speed compared to state-of-the-art methods.
  • Experimental results on real-world networks validated the proposed approach.

Conclusions:

  • PSAIIM offers an effective and computationally efficient solution for influence maximization.
  • Incorporating social behavior and user interests enhances the accuracy of identifying influential nodes.
  • The parallel approach using community structure is crucial for handling large-scale social networks.