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Identifying Influential Nodes Using a Shell-Based Ranking and Filtering Method in Social Networks.

Hamid Ahmadi Beni1, Asgarali Bouyer1

  • 1Department of Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran.

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Summary
This summary is machine-generated.

This study introduces a new shell-based ranking and filtering method (SRFM) to find influential nodes for maximizing information spread in social networks. SRFM improves efficiency and runtime compared to existing algorithms.

Keywords:
influence maximizationperiphery nodeseed nodeshell-based rankingsocial network

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Area of Science:

  • Social Network Analysis
  • Information Diffusion
  • Algorithm Design

Background:

  • Influence Maximization Problem (IMP) aims to identify k nodes for maximum spread.
  • IMP is NP-hard, necessitating heuristic approaches.
  • Existing methods often focus solely on high-core nodes, potentially overlooking influential nodes in other network layers.

Purpose of the Study:

  • To propose a novel shell-based ranking and filtering method (SRFM) for effective seed node selection in influence maximization.
  • To enhance the efficiency and accuracy of influence spread prediction by considering nodes across different network shells.
  • To reduce computational overhead through a pruning approach.

Main Methods:

  • The SRFM algorithm identifies nodes within different network shells.
  • A pruning strategy is employed to eliminate peripheral nodes, reducing computational load.
  • Seed nodes are selected from a candidate set, prioritizing "bridge" nodes that connect different network components.

Main Results:

  • The SRFM algorithm demonstrated superior efficiency in terms of influence spread compared to other methods.
  • Experimental results on synthetic and real datasets confirmed SRFM's improved runtime performance.
  • The method effectively leverages nodes from various network shells, not just the highest core.

Conclusions:

  • SRFM offers a more effective approach to influence maximization by considering a broader range of potentially influential nodes.
  • The algorithm provides a balance between computational efficiency and maximizing information diffusion.
  • This shell-based strategy enhances seed selection for social network influence strategies.