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Budget-aware local influence iterative algorithm for efficient influence maximization in social networks.

Lingfei Li1, Yingxin Song2, Wei Yang1

  • 1School of Management, Hangzhou Dianzi University, Hangzhou, 310018, China.

Heliyon
|November 18, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new cost model and the budget-aware local influence iterative (BLII) algorithm to efficiently solve the budgeted influence maximization (BIM) problem. The BLII algorithm significantly improves influence spread compared to existing methods.

Keywords:
Budgeted influence maximizationCost modelProxy-based algorithmSocial networks

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Area of Science:

  • Social Network Analysis
  • Computational Social Science
  • Information Science

Background:

  • The budgeted influence maximization (BIM) problem seeks optimal seed nodes within budget constraints.
  • Existing BIM algorithms struggle to balance speed and accuracy.
  • A refined cost model and efficient algorithm are needed for practical BIM applications.

Purpose of the Study:

  • To develop an efficient algorithm for the budgeted influence maximization (BIM) problem.
  • To propose a refined cost model based on empirical social media data.
  • To enhance the balance between timeliness and effectiveness in influence maximization.

Main Methods:

  • Developed a refined cost model using empirical analysis of Weibo quote data.
  • Introduced the budget-aware local influence iterative (BLII) algorithm, a proxy-based approach.
  • Approximated global influence using one-hop influence and managed overlap via iterative updates.

Main Results:

  • The BLII algorithm demonstrated superior effectiveness and efficiency in comparative experiments.
  • BLII outperformed other proxy-based algorithms by 20%-255% in influence spread.
  • Achieved 96% improvement over the state-of-the-art simulation-based approach with reasonable running time.

Conclusions:

  • The proposed cost model and BLII algorithm offer novel insights for BIM problems.
  • BLII provides a potent and efficient tool for identifying seed nodes in budgeted influence maximization.
  • The study addresses the critical need for timely and effective solutions in network influence analysis.