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Identifying vital nodes for influence maximization in attributed networks.

Ying Wang1, Yunan Zheng2, Yiguang Liu1

  • 1College of Computer Science, Sichuan University, Chengdu, 610065, Sichuan, China.

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This study introduces a new community-based method for influence maximization in attributed networks. It effectively identifies vital nodes by considering both network topology and node attributes, improving information spread.

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Area of Science:

  • Network science
  • Computational social science

Background:

  • Influence maximization is crucial in network science, often relying solely on network topology.
  • Node attributes are vital for influence in attributed networks but are often overlooked.
  • Existing methods may not fully capture influence in networks with rich node attributes.

Purpose of the Study:

  • To propose an extended linear threshold (LT) model for simulating information propagation in attributed networks.
  • To develop a novel community-based method for influence maximization in attributed networks.
  • To identify vital nodes by integrating both topological and attribute influences.

Main Methods:

  • An extension of the linear threshold (LT) propagation model was developed.
  • A community-based approach was proposed to identify influential nodes.
  • The method integrates network topology and node attribute information.

Main Results:

  • The proposed method was evaluated on five real-world and one synthetic network.
  • Experimental results demonstrated significant improvements in influence spread compared to existing methods (CELF, IMM, CoFIM, HGD, NCVoteRank, K-Shell).
  • The method shows superior performance across different propagation models.

Conclusions:

  • The novel community-based method effectively identifies vital nodes in attributed networks.
  • Considering both topology and attribute influence enhances the accuracy of influence maximization.
  • This approach offers a more suitable solution for influence spread in attributed networks.